Periocular test for mixed reality calibration

ABSTRACT

A wearable device can include an inward-facing imaging system configured to acquire images of a user&#39;s periocular region. The wearable device can determine a relative position between the wearable device and the user&#39;s face based on the images acquired by the inward-facing imaging system. The relative position may be used to determine whether the user is wearing the wearable device, whether the wearable device fits the user, or whether an adjustment to a rendering location of virtual object should be made to compensate for a deviation of the wearable device from its normal resting position.

INCORPORATION BY REFERENCE TO ANY PRIORITY APPLICATIONS

This application claims the benefit of priority under 35 U.S.C. § 119(e)to U.S. Provisional Application No. 62/404,419, filed on Oct. 5, 2016,entitled “PERIOCULAR TEST FOR GLASSES REMOVAL”, U.S. ProvisionalApplication No. 62/404,493, filed on Oct. 5, 2016, entitled “PERIOCULARTEST FOR GLASSES FIT”, and U.S. Provisional Application No. 62/416,341,filed on Nov. 2, 2016, entitled “DYNAMIC DISPLAY CORRECTION BASED ONDISPLAY POSITION TRACKING”, the disclosures of all which are herebyincorporated by reference herein in their entireties.

FIELD

The present disclosure relates to virtual reality and augmented realityimaging and visualization systems and more particularly to tuningoperational parameters of a virtual or augmented reality wearabledisplay device.

BACKGROUND

Modern computing and display technologies have facilitated thedevelopment of systems for so called “virtual reality”, “augmentedreality”, or “mixed reality” experiences, wherein digitally reproducedimages or portions thereof are presented to a user in a manner whereinthey seem to be, or may be perceived as, real. A virtual reality, or“VR”, scenario typically involves presentation of digital or virtualimage information without transparency to other actual real-world visualinput; an augmented reality, or “AR”, scenario typically involvespresentation of digital or virtual image information as an augmentationto visualization of the actual world around the user; a mixed reality,or “MR”, related to merging real and virtual worlds to produce newenvironments where physical and virtual objects co-exist and interact inreal time. As it turns out, the human visual perception system is verycomplex, and producing a VR, AR, or MR technology that facilitates acomfortable, natural-feeling, rich presentation of virtual imageelements amongst other virtual or real-world imagery elements ischallenging. Systems and methods disclosed herein address variouschallenges related to VR, AR and MR technology.

SUMMARY

A wearable device can include an inward-facing imaging system configuredto acquire images of a user's periocular region. The wearable device candetermine a relative position between the wearable device and the user'sface based on the images acquired by the inward-facing imaging system.The relative position may be used to determine whether the user iswearing the wearable device, whether the wearable device fits the user,or whether an adjustment to a rendering location of virtual objectshould be made to compensate for a deviation of the wearable device fromits normal resting position.

Details of one or more implementations of the subject matter describedin this specification are set forth in the accompanying drawings and thedescription below. Other features, aspects, and advantages will becomeapparent from the description, the drawings, and the claims. Neitherthis summary nor the following detailed description purports to defineor limit the scope of the inventive subject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts an illustration of a mixed reality scenario with certainvirtual reality objects, and certain physical objects viewed by aperson.

FIG. 2 schematically illustrates an example of a wearable system.

FIG. 3 schematically illustrates aspects of an approach for simulatingthree-dimensional imagery using multiple depth planes.

FIG. 4 schematically illustrates an example of a waveguide stack foroutputting image information to a user.

FIG. 5 shows example exit beams that may be outputted by a waveguide.

FIG. 6 is a schematic diagram showing an optical system including awaveguide apparatus, an optical coupler subsystem to optically couplelight to or from the waveguide apparatus, and a control subsystem, usedin the generation of a multi-focal volumetric display, image, or lightfield.

FIG. 7 is a block diagram of an example of a wearable system.

FIG. 8 is a process flow diagram of an example of a method of renderingvirtual content in relation to recognized objects.

FIG. 9 is a block diagram of another example of a wearable system.

FIG. 10 is a process flow diagram of an example of a method forinteracting with a virtual user interface.

FIG. 11 illustrates an example wearable device which can acquire imagesof the user's face.

FIG. 12A illustrates an example image of a periocular region for oneeye.

FIG. 12B illustrates another example image of the periocular region,where a portion of the periocular region in the image is masked out.

FIG. 13A illustrates an example where a head-mounted display is at itsnormal resting position with respect to the user's face.

FIG. 13B illustrates an example where the head-mounted display is tiltedto one side.

FIG. 13C illustrates an example where the head-mounted display hastitled or shifted forward.

FIGS. 14A and 14B illustrate an example of adjusting a renderinglocation of a virtual object in a spatial augmented reality (SAR)display.

FIG. 15A illustrates an example method for determining a fit of thewearable device on a user's face.

FIG. 15B illustrates an example of a method for using a machine learningtechnique to provide a mapping for goodness of fit or whether thehead-mounted display is on the user.

FIG. 15C illustrates an example method for determining removal of thewearable device from a user's head.

FIG. 16 illustrates an example process for adjusting a renderinglocation of a virtual object.

Throughout the drawings, reference numbers may be re-used to indicatecorrespondence between referenced elements. The drawings are provided toillustrate example embodiments described herein and are not intended tolimit the scope of the disclosure.

DETAILED DESCRIPTION Overview

A wearable device for an AR/VR/MR system can be a head-mounted device(HMD) for presenting three-dimensional (3D) images to a user. An HMD mayinclude a head-mounted display which can render a three-dimensional (3D)virtual object into the user's environment from the perspective of theuser's eyes. As a result, the 3D virtual object may be perceived by theuser in a similar manner as the real world objects. The HMD can renderthe 3D virtual object based on a world map which indicates the objects(including virtual objects) in the user's environment. The HMD canilluminate pixels on the display with a color and intensitycorresponding to the world map. However, a point in the world map maynot have a predetermined rendering location on an HMD since the user'seyes move around. Although the display may be calibrated relative to theuser's eyes, such as when the device is first used by the user, suchcalibration may not always be reliable because the display will not bestrongly affixed to the user's head. For example, the display can movewhen the user is interacting with it, such as when a user is playing avideo game that requires user movement. Further, the display may slipslightly down the user's nose or tilt relative to a line between theuser's ears. As a result, the HMD may not be able to provide a realisticpresentation of the virtual object due to the shift (such as tiltingforward or to one side) of the display.

The techniques described herein are at least in part directed to solvingthis problem. The inward-facing imaging system of the wearable devicecan acquire images of the periocular region of the user's face. Thewearable device can analyze the periocular images to identify periocularfeatures (e.g., position of the user's eyes). The wearable device cantrack the periocular features to determine the relative position betweenthe user's eyes and the HMD. Based on this information, the wearabledevice can dynamically adjust the rendering location of a virtual object(to be displayed by the HMD) to reflect the perspectives of the user'seyes. Accordingly, such embodiments of the HMD can accurately displayimages to the user even when the HMD slips, moves, or tilts slightlyrelative to the user's head.

The relative position between the HMD and the user's head can also beused to determine a fit of HMD. The fit may provide an indication onwhether to adjust certain parameters of the HMD (e.g., renderingparameters or the position of the frame (e.g., by increasing ordecreasing the distances between the left and right ear stems toaccommodate a larger or smaller head)) to provide a realistic andimmersive visual experience. The HMD can use a mapping from an eye-imagespace of the periocular region to a fit space for the device todetermine goodness of fit. The eye-image space may be determined basedon images acquired by the inward-facing imaging system, such as forexample, images of periocular regions or features. The fit space caninclude a collection of qualitative or quantities indications fordegrees of fit. The mapping may be learned by a machine learningtechnique such as, e.g., a deep neural network, to identify features inthe user's periocular region and use the identified features todetermine relative positions between the HMD and the user's face or toclassify goodness of fit. The HMD can provide an indication on whetherthe HMD fits the user's face based on the relative position or otherfeatures learned by the machine learning technique. The HMD can alsoadjust the projection of light from the 3D display based on the relativeposition of the HMD with respect to the user's head so that the light(e.g., a light field) is accurately projected into each of the user'seyes.

The HMD can also use the mapping to determine whether the user iswearing the HMD. For example, when the HMD determines that theperiocular features do not appear in the images acquired by theinward-facing imaging system (or are too small, indicating the HMD isoff the user's face), the HMD may send a signal indicating that the userhas taken off the device. The signal may cause the device to change fromone mode to another. For example, the signal may cause the HMD to changefrom an active mode to a powered off mode or a sleep mode. As anotherexample, the HMD can use the images to calculate the distance betweenthe user's face and the device; and if the HMD determines that thedistance is greater than a threshold distance, the HMD may send a signalindicating that the user has taken off the HMD.

Examples of 3D Display of a Wearable System

A wearable system (also referred to herein as an augmented reality (AR)system) can be configured to present 2D or 3D virtual images to a user.The images may be still images, frames of a video, or a video, incombination or the like. At least a portion of the wearable system canbe implemented on a wearable device that can present a VR, AR, or MRenvironment, alone or in combination, for user interaction. The wearabledevice can be a head-mounted device (HMD) which is used interchangeablyas an AR device (ARD). Further, for the purpose of the presentdisclosure, the term “AR” is used interchangeably with the term “MR”.

FIG. 1 depicts an illustration of a mixed reality scenario with certainvirtual reality objects, and certain physical objects viewed by aperson. In FIG. 1, an MR scene 100 is depicted wherein a user of an MRtechnology sees a real-world park-like setting 110 featuring people,trees, buildings in the background, and a concrete platform 120. Inaddition to these items, the user of the MR technology also perceivesthat he “sees” a robot statue 130 standing upon the real-world platform120, and a cartoon-like avatar character 140 flying by which seems to bea personification of a bumble bee, even though these elements do notexist in the real world.

In order for the 3D display to produce a true sensation of depth, andmore specifically, a simulated sensation of surface depth, it may bedesirable for each point in the display's visual field to generate anaccommodative response corresponding to its virtual depth. If theaccommodative response to a display point does not correspond to thevirtual depth of that point, as determined by the binocular depth cuesof convergence and stereopsis, the human eye may experience anaccommodation conflict, resulting in unstable imaging, harmful eyestrain, headaches, and, in the absence of accommodation information,almost a complete lack of surface depth.

VR, AR, and MR experiences can be provided by display systems havingdisplays in which images corresponding to a plurality of depth planesare provided to a viewer. The images may be different for each depthplane (e.g., provide slightly different presentations of a scene orobject) and may be separately focused by the viewer's eyes, therebyhelping to provide the user with depth cues based on the accommodationof the eye required to bring into focus different image features for thescene located on different depth plane or based on observing differentimage features on different depth planes being out of focus. Asdiscussed elsewhere herein, such depth cues provide credible perceptionsof depth.

FIG. 2 illustrates an example of wearable system 200 which can beconfigured to provide an AR/VR/MR scene. The wearable system 200 canalso be referred to as the AR system 200. The wearable system 200includes a display 220, and various mechanical and electronic modulesand systems to support the functioning of display 220. The display 220may be coupled to a frame 230, which is wearable by a user, wearer, orviewer 210. The display 220 can be positioned in front of the eyes ofthe user 210. The display 220 can present AR/VR/MR content to a user.The display 220 can comprise a head mounted display that is worn on thehead of the user. The head mounted display may be a heads-up display(HUD) which can display virtual information in pre-determined locationswithin a field of view of the user (as perceived through the HUD). Thehead-mounted display may also be a spatial augmented reality (SAR)display which can render 3D objects into the user's environment in aperspective correct manner (e.g., from the perspective of the user) suchthat the virtual objects appear similar to the real world objects. Theperspective used for rendering the virtual objects may also be referredto as rendering viewpoint.

In some embodiments, a speaker 240 is coupled to the frame 230 andpositioned adjacent the ear canal of the user (in some embodiments,another speaker, not shown, is positioned adjacent the other ear canalof the user to provide for stereo/shapeable sound control). The display220 can include an audio sensor (e.g., a microphone) 232 for detectingan audio stream from the environment and capture ambient sound. In someembodiments, one or more other audio sensors, not shown, are positionedto provide stereo sound reception. Stereo sound reception can be used todetermine the location of a sound source. The wearable system 200 canperform voice or speech recognition on the audio stream.

The wearable system 200 can include an outward-facing imaging system 464(shown in FIG. 4) which observes the world in the environment around theuser. The wearable system 200 can also include an inward-facing imagingsystem 462 (shown in FIG. 4) which can track the eye movements of theuser. The inward-facing imaging system may track either one eye'smovements or both eyes' movements. The inward-facing imaging system 462may be attached to the frame 230 and may be in electrical communicationwith the processing modules 260 or 270, which may process imageinformation acquired by the inward-facing imaging system to determine,e.g., the pupil diameters or orientations of the eyes, eye movements oreye pose of the user 210. The inward-facing imaging system 462 mayinclude one or more cameras. For example, at least one camera may beused to image each eye. The images acquired by the cameras may be usedto determine pupil size or eye pose for each eye separately, therebyallowing presentation of image information to each eye to be dynamicallytailored to that eye. As another example, the pupil diameter ororientation of only one eye is determined (e.g., based on imagesacquired for a camera configured to acquire the images of that eye) andthe eye features determined for this eye are assumed to be similar forthe other eye of the user 210.

As an example, the wearable system 200 can use the outward-facingimaging system 464 or the inward-facing imaging system 462 to acquireimages of a pose of the user. The images may be still images, frames ofa video, or a video.

The display 220 can be operatively coupled 250, such as by a wired leador wireless connectivity, to a local data processing module 260 whichmay be mounted in a variety of configurations, such as fixedly attachedto the frame 230, fixedly attached to a helmet or hat worn by the user,embedded in headphones, or otherwise removably attached to the user 210(e.g., in a backpack-style configuration, in a belt-coupling styleconfiguration).

The local processing and data module 260 may comprise a hardwareprocessor, as well as digital memory, such as non-volatile memory (e.g.,flash memory), both of which may be utilized to assist in theprocessing, caching, and storage of data. The data may include data a)captured from sensors (which may be, e.g., operatively coupled to theframe 230 or otherwise attached to the user 210), such as image capturedevices (e.g., cameras in the inward-facing imaging system or theoutward-facing imaging system), audio sensors (e.g., microphones),inertial measurement units (IMUs), accelerometers, compasses, globalpositioning system (GPS) units, radio devices, or gyroscopes; or b)acquired or processed using remote processing module 270 or remote datarepository 280, possibly for passage to the display 220 after suchprocessing or retrieval. The local processing and data module 260 may beoperatively coupled by communication links 262 or 264, such as via wiredor wireless communication links, to the remote processing module 270 orremote data repository 280 such that these remote modules are availableas resources to the local processing and data module 260. In addition,remote processing module 280 and remote data repository 280 may beoperatively coupled to each other.

In some embodiments, the remote processing module 270 may comprise oneor more processors configured to analyze and process data or imageinformation. In some embodiments, the remote data repository 280 maycomprise a digital data storage facility, which may be available throughthe internet or other networking configuration in a “cloud” resourceconfiguration. In some embodiments, all data is stored and allcomputations are performed in the local processing and data module,allowing fully autonomous use from a remote module.

The human visual system is complicated and providing a realisticperception of depth is challenging. Without being limited by theory, itis believed that viewers of an object may perceive the object as beingthree-dimensional due to a combination of vergence and accommodation.Vergence movements (i.e., rolling movements of the pupils toward or awayfrom each other to converge the lines of sight of the eyes to fixateupon an object) of the two eyes relative to each other are closelyassociated with focusing (or “accommodation”) of the lenses of the eyes.Under normal conditions, changing the focus of the lenses of the eyes,or accommodating the eyes, to change focus from one object to anotherobject at a different distance will automatically cause a matchingchange in vergence to the same distance, under a relationship known asthe “accommodation-vergence reflex.” Likewise, a change in vergence willtrigger a matching change in accommodation, under normal conditions.Display systems that provide a better match between accommodation andvergence may form more realistic and comfortable simulations ofthree-dimensional imagery.

FIG. 3 illustrates aspects of an approach for simulating athree-dimensional imagery using multiple depth planes. With reference toFIG. 3, objects at various distances from eyes 302 and 304 on the z-axisare accommodated by the eyes 302 and 304 so that those objects are infocus. The eyes 302 and 304 assume particular accommodated states tobring into focus objects at different distances along the z-axis.Consequently, a particular accommodated state may be said to beassociated with a particular one of depth planes 306, which has anassociated focal distance, such that objects or parts of objects in aparticular depth plane are in focus when the eye is in the accommodatedstate for that depth plane. In some embodiments, three-dimensionalimagery may be simulated by providing different presentations of animage for each of the eyes 302 and 304, and also by providing differentpresentations of the image corresponding to each of the depth planes.While shown as being separate for clarity of illustration, it will beappreciated that the fields of view of the eyes 302 and 304 may overlap,for example, as distance along the z-axis increases. In addition, whileshown as flat for the ease of illustration, it will be appreciated thatthe contours of a depth plane may be curved in physical space, such thatall features in a depth plane are in focus with the eye in a particularaccommodated state. Without being limited by theory, it is believed thatthe human eye typically can interpret a finite number of depth planes toprovide depth perception. Consequently, a highly believable simulationof perceived depth may be achieved by providing, to the eye, differentpresentations of an image corresponding to each of these limited numberof depth planes.

Waveguide Stack Assembly

FIG. 4 illustrates an example of a waveguide stack for outputting imageinformation to a user. A wearable system 400 includes a stack ofwaveguides, or stacked waveguide assembly 480 that may be utilized toprovide three-dimensional perception to the eye/brain using a pluralityof waveguides 432 b, 434 b, 436 b, 438 b, 4400 b. In some embodiments,the wearable system 400 may correspond to wearable system 200 of FIG. 2,with FIG. 4 schematically showing some parts of that wearable system 200in greater detail. For example, in some embodiments, the waveguideassembly 480 may be integrated into the display 220 of FIG. 2.

With continued reference to FIG. 4, the waveguide assembly 480 may alsoinclude a plurality of features 458, 456, 454, 452 between thewaveguides. In some embodiments, the features 458, 456, 454, 452 may belenses. In other embodiments, the features 458, 456, 454, 452 may not belenses. Rather, they may simply be spacers (e.g., cladding layers orstructures for forming air gaps).

The waveguides 432 b, 434 b, 436 b, 438 b, 440 b or the plurality oflenses 458, 456, 454, 452 may be configured to send image information tothe eye with various levels of wavefront curvature or light raydivergence. Each waveguide level may be associated with a particulardepth plane and may be configured to output image informationcorresponding to that depth plane. Image injection devices 420, 422,424, 426, 428 may be utilized to inject image information into thewaveguides 440 b, 438 b, 436 b, 434 b, 432 b, each of which may beconfigured to distribute incoming light across each respectivewaveguide, for output toward the eye 410 (which may correspond to theeye 304 in FIG. 3). Light exits an output surface of the image injectiondevices 420, 422, 424, 426, 428 and is injected into a correspondinginput edge of the waveguides 440 b, 438 b, 436 b, 434 b, 432 b. In someembodiments, a single beam of light (e.g., a collimated beam) may beinjected into each waveguide to output an entire field of clonedcollimated beams that are directed toward the eye 410 at particularangles (and amounts of divergence) corresponding to the depth planeassociated with a particular waveguide.

In some embodiments, the image injection devices 420, 422, 424, 426, 428are discrete displays that each produce image information for injectioninto a corresponding waveguide 440 b, 438 b, 436 b, 434 b, 432 b,respectively. In some other embodiments, the image injection devices420, 422, 424, 426, 428 are the output ends of a single multiplexeddisplay which may, e.g., pipe image information via one or more opticalconduits (such as fiber optic cables) to each of the image injectiondevices 420, 422, 424, 426, 428.

A controller 460 controls the operation of the stacked waveguideassembly 480 and the image injection devices 420, 422, 424, 426, 428.The controller 460 includes programming (e.g., instructions in anon-transitory computer-readable medium) that regulates the timing andprovision of image information to the waveguides 440 b, 438 b, 436 b,434 b, 432 b. In some embodiments, the controller 460 may be a singleintegral device, or a distributed system connected by wired or wirelesscommunication channels. The controller 460 may be part of the processingmodules 260 or 270 (illustrated in FIG. 2) in some embodiments.

The waveguides 440 b, 438 b, 436 b, 434 b, 432 b may be configured topropagate light within each respective waveguide by total internalreflection (TIR). The waveguides 440 b, 438 b, 436 b, 434 b, 432 b mayeach be planar or have another shape (e.g., curved), with major top andbottom surfaces and edges extending between those major top and bottomsurfaces. In the illustrated configuration, the waveguides 440 b, 438 b,436 b, 434 b, 432 b may each include light extracting optical elements440 a, 438 a, 436 a, 434 a, 432 a that are configured to extract lightout of a waveguide by redirecting the light, propagating within eachrespective waveguide, out of the waveguide to output image informationto the eye 410. Extracted light may also be referred to as outcoupledlight, and light extracting optical elements may also be referred to asoutcoupling optical elements. An extracted beam of light is outputted bythe waveguide at locations at which the light propagating in thewaveguide strikes a light redirecting element. The light extractingoptical elements (440 a, 438 a, 436 a, 434 a, 432 a) may, for example,be reflective or diffractive optical features. While illustrateddisposed at the bottom major surfaces of the waveguides 440 b, 438 b,436 b, 434 b, 432 b for ease of description and drawing clarity, in someembodiments, the light extracting optical elements 440 a, 438 a, 436 a,434 a, 432 a may be disposed at the top or bottom major surfaces, or maybe disposed directly in the volume of the waveguides 440 b, 438 b, 436b, 434 b, 432 b. In some embodiments, the light extracting opticalelements 440 a, 438 a, 436 a, 434 a, 432 a may be formed in a layer ofmaterial that is attached to a transparent substrate to form thewaveguides 440 b, 438 b, 436 b, 434 b, 432 b. In some other embodiments,the waveguides 440 b, 438 b, 436 b, 434 b, 432 b may be a monolithicpiece of material and the light extracting optical elements 440 a, 438a, 436 a, 434 a, 432 a may be formed on a surface or in the interior ofthat piece of material.

With continued reference to FIG. 4, as discussed herein, each waveguide440 b, 438 b, 436 b, 434 b, 432 b is configured to output light to forman image corresponding to a particular depth plane. For example, thewaveguide 432 b nearest the eye may be configured to deliver collimatedlight, as injected into such waveguide 432 b, to the eye 410. Thecollimated light may be representative of the optical infinity focalplane. The next waveguide up 434 b may be configured to send outcollimated light which passes through the first lens 452 (e.g., anegative lens) before it can reach the eye 410. First lens 452 may beconfigured to create a slight convex wavefront curvature so that theeye/brain interprets light coming from that next waveguide up 434 b ascoming from a first focal plane closer inward toward the eye 410 fromoptical infinity. Similarly, the third up waveguide 436 b passes itsoutput light through both the first lens 452 and second lens 454 beforereaching the eye 410. The combined optical power of the first and secondlenses 452 and 454 may be configured to create another incrementalamount of wavefront curvature so that the eye/brain interprets lightcoming from the third waveguide 436 b as coming from a second focalplane that is even closer inward toward the person from optical infinitythan was light from the next waveguide up 434 b.

The other waveguide layers (e.g., waveguides 438 b, 440 b) and lenses(e.g., lenses 456, 458) are similarly configured, with the highestwaveguide 440 b in the stack sending its output through all of thelenses between it and the eye for an aggregate focal powerrepresentative of the closest focal plane to the person. To compensatefor the stack of lenses 458, 456, 454, 452 when viewing/interpretinglight coming from the world 470 on the other side of the stackedwaveguide assembly 480, a compensating lens layer 430 may be disposed atthe top of the stack to compensate for the aggregate power of the lensstack 458, 456, 454, 452 below. Such a configuration provides as manyperceived focal planes as there are available waveguide/lens pairings.Both the light extracting optical elements of the waveguides and thefocusing aspects of the lenses may be static (e.g., not dynamic orelectro-active). In some alternative embodiments, either or both may bedynamic using electro-active features.

With continued reference to FIG. 4, the light extracting opticalelements 440 a, 438 a, 436 a, 434 a, 432 a may be configured to bothredirect light out of their respective waveguides and to output thislight with the appropriate amount of divergence or collimation for aparticular depth plane associated with the waveguide. As a result,waveguides having different associated depth planes may have differentconfigurations of light extracting optical elements, which output lightwith a different amount of divergence depending on the associated depthplane. In some embodiments, as discussed herein, the light extractingoptical elements 440 a, 438 a, 436 a, 434 a, 432 a may be volumetric orsurface features, which may be configured to output light at specificangles. For example, the light extracting optical elements 440 a, 438 a,436 a, 434 a, 432 a may be volume holograms, surface holograms, and/ordiffraction gratings. Light extracting optical elements, such asdiffraction gratings, are described in U.S. Patent Publication No.2015/0178939, published Jun. 25, 2015, which is incorporated byreference herein in its entirety.

In some embodiments, the light extracting optical elements 440 a, 438 a,436 a, 434 a, 432 a are diffractive features that form a diffractionpattern, or “diffractive optical element” (also referred to herein as a“DOE”). Preferably, the DOE has a relatively low diffraction efficiencyso that only a portion of the light of the beam is deflected away towardthe eye 410 with each intersection of the DOE, while the rest continuesto move through a waveguide via total internal reflection. The lightcarrying the image information can thus be divided into a number ofrelated exit beams that exit the waveguide at a multiplicity oflocations and the result is a fairly uniform pattern of exit emissiontoward the eye 304 for this particular collimated beam bouncing aroundwithin a waveguide.

In some embodiments, one or more DOEs may be switchable between “on”state in which they actively diffract, and “off” state in which they donot significantly diffract. For instance, a switchable DOE may comprisea layer of polymer dispersed liquid crystal, in which microdropletscomprise a diffraction pattern in a host medium, and the refractiveindex of the microdroplets can be switched to substantially match therefractive index of the host material (in which case the pattern doesnot appreciably diffract incident light) or the microdroplet can beswitched to an index that does not match that of the host medium (inwhich case the pattern actively diffracts incident light).

In some embodiments, the number and distribution of depth planes ordepth of field may be varied dynamically based on the pupil sizes ororientations of the eyes of the viewer. Depth of field may changeinversely with a viewer's pupil size. As a result, as the sizes of thepupils of the viewer's eyes decrease, the depth of field increases suchthat one plane that is not discernible because the location of thatplane is beyond the depth of focus of the eye may become discernible andappear more in focus with reduction of pupil size and commensurate withthe increase in depth of field. Likewise, the number of spaced apartdepth planes used to present different images to the viewer may bedecreased with the decreased pupil size. For example, a viewer may notbe able to clearly perceive the details of both a first depth plane anda second depth plane at one pupil size without adjusting theaccommodation of the eye away from one depth plane and to the otherdepth plane. These two depth planes may, however, be sufficiently infocus at the same time to the user at another pupil size withoutchanging accommodation.

In some embodiments, the display system may vary the number ofwaveguides receiving image information based upon determinations ofpupil size or orientation, or upon receiving electrical signalsindicative of particular pupil size or orientation. For example, if theuser's eyes are unable to distinguish between two depth planesassociated with two waveguides, then the controller 460 (which may be anembodiment of the local processing and data module 260) can beconfigured or programmed to cease providing image information to one ofthese waveguides. Advantageously, this may reduce the processing burdenon the system, thereby increasing the responsiveness of the system. Inembodiments in which the DOEs for a waveguide are switchable between theon and off states, the DOEs may be switched to the off state when thewaveguide does receive image information.

In some embodiments, it may be desirable to have an exit beam meet thecondition of having a diameter that is less than the diameter of the eyeof a viewer. However, meeting this condition may be challenging in viewof the variability in size of the viewer's pupils. In some embodiments,this condition is met over a wide range of pupil sizes by varying thesize of the exit beam in response to determinations of the size of theviewer's pupil. For example, as the pupil size decreases, the size ofthe exit beam may also decrease. In some embodiments, the exit beam sizemay be varied using a variable aperture.

The wearable system 400 can include an outward-facing imaging system 464(e.g., a digital camera) that images a portion of the world 470. Thisportion of the world 470 may be referred to as the field of view (FOV)of a world camera and the imaging system 464 is sometimes referred to asan FOV camera. The FOV of the world camera may or may not be the same asthe FOV of a viewer 210 which encompasses a portion of the world 470 theviewer 210 perceives at a given time. For example, in some situations,the FOV of the world camera may be larger than the viewer 210 of theviewer 210 of the wearable system 400. The entire region available forviewing or imaging by a viewer may be referred to as the field of regard(FOR). The FOR may include 4π steradians of solid angle surrounding thewearable system 400 because the wearer can move his body, head, or eyesto perceive substantially any direction in space. In other contexts, thewearer's movements may be more constricted, and accordingly the wearer'sFOR may subtend a smaller solid angle. Images obtained from theoutward-facing imaging system 464 can be used to track gestures made bythe user (e.g., hand or finger gestures), detect objects in the world470 in front of the user, and so forth.

The wearable system 400 can include an audio sensor 232, e.g., amicrophone, to capture ambient sound. As described above, in someembodiments, one or more other audio sensors can be positioned toprovide stereo sound reception useful to the determination of locationof a speech source. The audio sensor 232 can comprise a directionalmicrophone, as another example, which can also provide such usefuldirectional information as to where the audio source is located. Thewearable system 400 can use information from both the outward-facingimaging system 464 and the audio sensor 230 in locating a source ofspeech, or to determine an active speaker at a particular moment intime, etc. For example, the wearable system 400 can use the voicerecognition alone or in combination with a reflected image of thespeaker (e.g., as seen in a mirror) to determine the identity of thespeaker. As another example, the wearable system 400 can determine aposition of the speaker in an environment based on sound acquired fromdirectional microphones. The wearable system 400 can parse the soundcoming from the speaker's position with speech recognition algorithms todetermine the content of the speech and use voice recognition techniquesto determine the identity (e.g., name or other demographic information)of the speaker.

The wearable system 400 can also include an inward-facing imaging system466 (e.g., a digital camera), which observes the movements of the user,such as the eye movements and the facial movements. The inward-facingimaging system 466 may be used to capture images of the eye 410 todetermine the size and/or orientation of the pupil of the eye 304. Theinward-facing imaging system 466 can be used to obtain images for use indetermining the direction the user is looking (e.g., eye pose) or forbiometric identification of the user (e.g., via iris identification). Insome embodiments, at least one camera may be utilized for each eye, toseparately determine the pupil size or eye pose of each eyeindependently, thereby allowing the presentation of image information toeach eye to be dynamically tailored to that eye. In some otherembodiments, the pupil diameter or orientation of only a single eye 410(e.g., using only a single camera per pair of eyes) is determined andassumed to be similar for both eyes of the user. The images obtained bythe inward-facing imaging system 466 may be analyzed to determine theuser's eye pose or mood, which can be used by the wearable system 400 todecide which audio or visual content should be presented to the user.The wearable system 400 may also determine head pose (e.g., headposition or head orientation) using sensors such as IMUs,accelerometers, gyroscopes, etc.

The wearable system 400 can include a user input device 466 by which theuser can input commands to the controller 460 to interact with thewearable system 400. For example, the user input device 466 can includea trackpad, a touchscreen, a joystick, a multiple degree-of-freedom(DOF) controller, a capacitive sensing device, a game controller, akeyboard, a mouse, a directional pad (D-pad), a wand, a haptic device, atotem (e.g., functioning as a virtual user input device), and so forth.A multi-DOF controller can sense user input in some or all possibletranslations (e.g., left/right, forward/backward, or up/down) orrotations (e.g., yaw, pitch, or roll) of the controller. A multi-DOFcontroller which supports the translation movements may be referred toas a 3DOF while a multi-DOF controller which supports the translationsand rotations may be referred to as 6DOF. In some cases, the user mayuse a finger (e.g., a thumb) to press or swipe on a touch-sensitiveinput device to provide input to the wearable system 400 (e.g., toprovide user input to a user interface provided by the wearable system400). The user input device 466 may be held by the user's hand duringthe use of the wearable system 400. The user input device 466 can be inwired or wireless communication with the wearable system 400.

FIG. 5 shows an example of exit beams outputted by a waveguide. Onewaveguide is illustrated, but it will be appreciated that otherwaveguides in the waveguide assembly 480 may function similarly, wherethe waveguide assembly 480 includes multiple waveguides. Light 520 isinjected into the waveguide 432 b at the input edge 432 c of thewaveguide 432 b and propagates within the waveguide 432 b by TIR. Atpoints where the light 520 impinges on the DOE 432 a, a portion of thelight exits the waveguide as exit beams 510. The exit beams 510 areillustrated as substantially parallel but they may also be redirected topropagate to the eye 410 at an angle (e.g., forming divergent exitbeams), depending on the depth plane associated with the waveguide 432b. It will be appreciated that substantially parallel exit beams may beindicative of a waveguide with light extracting optical elements thatoutcouple light to form images that appear to be set on a depth plane ata large distance (e.g., optical infinity) from the eye 410. Otherwaveguides or other sets of light extracting optical elements may outputan exit beam pattern that is more divergent, which would require the eye410 to accommodate to a closer distance to bring it into focus on theretina and would be interpreted by the brain as light from a distancecloser to the eye 410 than optical infinity.

FIG. 6 is a schematic diagram showing an optical system including awaveguide apparatus, an optical coupler subsystem to optically couplelight to or from the waveguide apparatus, and a control subsystem, usedin the generation of a multi-focal volumetric display, image, or lightfield. The optical system can include a waveguide apparatus, an opticalcoupler subsystem to optically couple light to or from the waveguideapparatus, and a control subsystem. The optical system can be used togenerate a multi-focal volumetric, image, or light field. The opticalsystem can include one or more primary planar waveguides 632 a (only oneis shown in FIG. 6) and one or more DOEs 632 b associated with each ofat least some of the primary waveguides 632 a. The planar waveguides 632b can be similar to the waveguides 432 b, 434 b, 436 b, 438 b, 440 bdiscussed with reference to FIG. 4. The optical system may employ adistribution waveguide apparatus to relay light along a first axis(vertical or Y-axis in view of FIG. 6), and expand the light's effectiveexit pupil along the first axis (e.g., Y-axis). The distributionwaveguide apparatus may, for example, include a distribution planarwaveguide 622 b and at least one DOE 622 a (illustrated by doubledash-dot line) associated with the distribution planar waveguide 622 b.The distribution planar waveguide 622 b may be similar or identical inat least some respects to the primary planar waveguide 632 b, having adifferent orientation therefrom. Likewise, at least one DOE 622 a may besimilar to or identical in at least some respects to the DOE 632 a. Forexample, the distribution planar waveguide 622 b or DOE 622 a may becomprised of the same materials as the primary planar waveguide 632 b orDOE 632 a, respectively. Embodiments of the optical display system 600shown in FIG. 6 can be integrated into the wearable system 200 shown inFIG. 2.

The relayed and exit-pupil expanded light may be optically coupled fromthe distribution waveguide apparatus into the one or more primary planarwaveguides 632 b. The primary planar waveguide 632 b can relay lightalong a second axis, preferably orthogonal to first axis (e.g.,horizontal or X-axis in view of FIG. 6). Notably, the second axis can bea non-orthogonal axis to the first axis. The primary planar waveguide632 b expands the light's effective exit pupil along that second axis(e.g., X-axis). For example, the distribution planar waveguide 622 b canrelay and expand light along the vertical or Y-axis, and pass that lightto the primary planar waveguide 632 b which can relay and expand lightalong the horizontal or X-axis.

The optical system may include one or more sources of colored light(e.g., red, green, and blue laser light) 610 which may be opticallycoupled into a proximal end of a single mode optical fiber 640. A distalend of the optical fiber 640 may be threaded or received through ahollow tube 642 of piezoelectric material. The distal end protrudes fromthe tube 642 as fixed-free flexible cantilever 644. The piezoelectrictube 642 can be associated with four quadrant electrodes (notillustrated). The electrodes may, for example, be plated on the outside,outer surface or outer periphery or diameter of the tube 642. A coreelectrode (not illustrated) may also be located in a core, center, innerperiphery or inner diameter of the tube 642.

Drive electronics 650, for example electrically coupled via wires 660,drive opposing pairs of electrodes to bend the piezoelectric tube 642 intwo axes independently. The protruding distal tip of the optical fiber644 has mechanical modes of resonance. The frequencies of resonance candepend upon a diameter, length, and material properties of the opticalfiber 644. By vibrating the piezoelectric tube 642 near a first mode ofmechanical resonance of the fiber cantilever 644, the fiber cantilever644 can be caused to vibrate, and can sweep through large deflections.

By stimulating resonant vibration in two axes, the tip of the fibercantilever 644 is scanned biaxially in an area filling two-dimensional(2D) scan. By modulating an intensity of light source(s) 610 insynchrony with the scan of the fiber cantilever 644, light emerging fromthe fiber cantilever 644 can form an image. Descriptions of such a setup are provided in U.S. Patent Publication No. 2014/0003762, which isincorporated by reference herein in its entirety.

A component of an optical coupler subsystem can collimate the lightemerging from the scanning fiber cantilever 644. The collimated lightcan be reflected by mirrored surface 648 into the narrow distributionplanar waveguide 622 b which contains the at least one diffractiveoptical element (DOE) 622 a. The collimated light can propagatevertically (relative to the view of FIG. 6) along the distributionplanar waveguide 622 b by TIR, and in doing so repeatedly intersectswith the DOE 622 a. The DOE 622 a preferably has a low diffractionefficiency. This can cause a fraction (e.g., 10%) of the light to bediffracted toward an edge of the larger primary planar waveguide 632 bat each point of intersection with the DOE 622 a, and a fraction of thelight to continue on its original trajectory down the length of thedistribution planar waveguide 622 b via TIR.

At each point of intersection with the DOE 622 a, additional light canbe diffracted toward the entrance of the primary waveguide 632 b. Bydividing the incoming light into multiple outcoupled sets, the exitpupil of the light can be expanded vertically by the DOE 622 a in thedistribution planar waveguide 622 b. This vertically expanded lightcoupled out of distribution planar waveguide 622 b can enter the edge ofthe primary planar waveguide 632 b.

Light entering primary waveguide 632 b can propagate horizontally(relative to the view of FIG. 6) along the primary waveguide 632 b viaTIR. As the light intersects with DOE 632 a at multiple points as itpropagates horizontally along at least a portion of the length of theprimary waveguide 632 b via TIR. The DOE 632 a may advantageously bedesigned or configured to have a phase profile that is a summation of alinear diffraction pattern and a radially symmetric diffractive pattern,to produce both deflection and focusing of the light. The DOE 632 a mayadvantageously have a low diffraction efficiency (e.g., 10%), so thatonly a portion of the light of the beam is deflected toward the eye ofthe view with each intersection of the DOE 632 a while the rest of thelight continues to propagate through the primary waveguide 632 b viaTIR.

At each point of intersection between the propagating light and the DOE632 a, a fraction of the light is diffracted toward the adjacent face ofthe primary waveguide 632 b allowing the light to escape the TIR, andemerge from the face of the primary waveguide 632 b. In someembodiments, the radially symmetric diffraction pattern of the DOE 632 aadditionally imparts a focus level to the diffracted light, both shapingthe light wavefront (e.g., imparting a curvature) of the individual beamas well as steering the beam at an angle that matches the designed focuslevel.

Accordingly, these different pathways can cause the light to be coupledout of the primary planar waveguide 632 b by a multiplicity of DOEs 632a at different angles, focus levels, or yielding different fill patternsat the exit pupil. Different fill patterns at the exit pupil can bebeneficially used to create a light field display with multiple depthplanes. Each layer in the waveguide assembly or a set of layers (e.g., 3layers) in the stack may be employed to generate a respective color(e.g., red, blue, green). Thus, for example, a first set of threeadjacent layers may be employed to respectively produce red, blue andgreen light at a first focal depth. A second set of three adjacentlayers may be employed to respectively produce red, blue and green lightat a second focal depth. Multiple sets may be employed to generate afull 3D or 4D color image light field with various focal depths.

Other Components of the Wearable System

In many implementations, the wearable system may include othercomponents in addition or in alternative to the components of thewearable system described above. The wearable system may, for example,include one or more haptic devices or components. The haptic devices orcomponents may be operable to provide a tactile sensation to a user. Forexample, the haptic devices or components may provide a tactilesensation of pressure or texture when touching virtual content (e.g.,virtual objects, virtual tools, other virtual constructs). The tactilesensation may replicate a feel of a physical object which a virtualobject represents, or may replicate a feel of an imagined object orcharacter (e.g., a dragon) which the virtual content represents. In someimplementations, haptic devices or components may be worn by the user(e.g., a user wearable glove). In some implementations, haptic devicesor components may be held by the user.

The wearable system may, for example, include one or more physicalobjects which are manipulable by the user to allow input or interactionwith the wearable system. These physical objects may be referred toherein as totems. Some totems may take the form of inanimate objects,such as for example, a piece of metal or plastic, a wall, a surface oftable. In certain implementations, the totems may not actually have anyphysical input structures (e.g., keys, triggers, joystick, trackball,rocker switch). Instead, the totem may simply provide a physicalsurface, and the wearable system may render a user interface so as toappear to a user to be on one or more surfaces of the totem. Forexample, the wearable system may render an image of a computer keyboardand trackpad to appear to reside on one or more surfaces of a totem. Forexample, the wearable system may render a virtual computer keyboard andvirtual trackpad to appear on a surface of a thin rectangular plate ofaluminum which serves as a totem. The rectangular plate does not itselfhave any physical keys or trackpad or sensors. However, the wearablesystem may detect user manipulation or interaction or touches with therectangular plate as selections or inputs made via the virtual keyboardor virtual trackpad. The user input device 466 (shown in FIG. 4) may bean embodiment of a totem, which may include a trackpad, a touchpad, atrigger, a joystick, a trackball, a rocker or virtual switch, a mouse, akeyboard, a multi-degree-of-freedom controller, or another physicalinput device. A user may use the totem, alone or in combination withposes, to interact with the wearable system or other users.

Examples of haptic devices and totems usable with the wearable devices,HMD, and display systems of the present disclosure are described in U.S.Patent Publication No. 2015/0016777, which is incorporated by referenceherein in its entirety.

Example Wearable Systems, Environments, and Interfaces

A wearable system may employ various mapping related techniques in orderto achieve high depth of field in the rendered light fields. In mappingout the virtual world, it is advantageous to know all the features andpoints in the real world to accurately portray virtual objects inrelation to the real world. To this end, FOV images captured from usersof the wearable system can be added to a world model by including newpictures that convey information about various points and features ofthe real world. For example, the wearable system can collect a set ofmap points (such as 2D points or 3D points) and find new map points torender a more accurate version of the world model. The world model of afirst user can be communicated (e.g., over a network such as a cloudnetwork) to a second user so that the second user can experience theworld surrounding the first user.

FIG. 7 is a block diagram of an example of an MR environment 700. The MRenvironment 700 may be configured to receive input (e.g., visual input702 from the user's wearable system, stationary input 704 such as roomcameras, sensory input 706 from various sensors, gestures, totems, eyetracking, user input from the user input device 466 etc.) from one ormore user wearable systems (e.g., wearable system 200 or display system220) or stationary room systems (e.g., room cameras, etc.). The wearablesystems can use various sensors (e.g., accelerometers, gyroscopes,temperature sensors, movement sensors, depth sensors, GPS sensors,inward-facing imaging system, outward-facing imaging system, etc.) todetermine the location and various other attributes of the environmentof the user. This information may further be supplemented withinformation from stationary cameras in the room that may provide imagesor various cues from a different point of view. The image data acquiredby the cameras (such as the room cameras and/or the cameras of theoutward-facing imaging system) may be reduced to a set of mappingpoints.

One or more object recognizers 708 can crawl through the received data(e.g., the collection of points) and recognize or map points, tagimages, attach semantic information to objects with the help of a mapdatabase 710. The map database 710 may comprise various points collectedover time and their corresponding objects. The various devices and themap database can be connected to each other through a network (e.g.,LAN, WAN, etc.) to access the cloud.

Based on this information and collection of points in the map database,the object recognizers 708 a to 708 n may recognize objects in anenvironment. For example, the object recognizers can recognize faces,persons, windows, walls, user input devices, televisions, documents(e.g., travel tickets, driver's license, passport as described in thesecurity examples herein), other objects in the user's environment, etc.One or more object recognizers may be specialized for object withcertain characteristics. For example, the object recognizer 708 a may beused to recognizer faces, while another object recognizer may be usedrecognize documents.

The object recognitions may be performed using a variety of computervision techniques. For example, the wearable system can analyze theimages acquired by the outward-facing imaging system 464 (shown in FIG.4) to perform scene reconstruction, event detection, video tracking,object recognition (e.g., persons or documents), object pose estimation,facial recognition (e.g., from a person in the environment or an imageon a document), learning, indexing, motion estimation, or image analysis(e.g., identifying indicia within documents such as photos, signatures,identification information, travel information, etc.), and so forth. Oneor more computer vision algorithms may be used to perform these tasks.Non-limiting examples of computer vision algorithms include:Scale-invariant feature transform (SIFT), speeded up robust features(SURF), oriented FAST and rotated BRIEF (ORB), binary robust invariantscalable keypoints (BRISK), fast retina keypoint (FREAK), Viola-Jonesalgorithm, Eigenfaces approach, Lucas-Kanade algorithm, Horn-Schunkalgorithm, Mean-shift algorithm, visual simultaneous location andmapping (vSLAM) techniques, a sequential Bayesian estimator (e.g.,Kalman filter, extended Kalman filter, etc.), bundle adjustment,Adaptive thresholding (and other thresholding techniques), IterativeClosest Point (ICP), Semi Global Matching (SGM), Semi Global BlockMatching (SGBM), Feature Point Histograms, various machine learningalgorithms (such as e.g., support vector machine, k-nearest neighborsalgorithm, Naive Bayes, neural network (including convolutional or deepneural networks), or other supervised/unsupervised models, etc.), and soforth.

The object recognitions can additionally or alternatively be performedby a variety of machine learning algorithms. Once trained, the machinelearning algorithm can be stored by the HMD. Some examples of machinelearning algorithms can include supervised or non-supervised machinelearning algorithms, including regression algorithms (such as, forexample, Ordinary Least Squares Regression), instance-based algorithms(such as, for example, Learning Vector Quantization), decision treealgorithms (such as, for example, classification and regression trees),Bayesian algorithms (such as, for example, Naive Bayes), clusteringalgorithms (such as, for example, k-means clustering), association rulelearning algorithms (such as, for example, a-priori algorithms),artificial neural network algorithms (such as, for example, Perceptron),deep learning algorithms (such as, for example, Deep Boltzmann Machine,or deep neural network), dimensionality reduction algorithms (such as,for example, Principal Component Analysis), ensemble algorithms (suchas, for example, Stacked Generalization), and/or other machine learningalgorithms. In some embodiments, individual models can be customized forindividual data sets. For example, the wearable device can generate orstore a base model. The base model may be used as a starting point togenerate additional models specific to a data type (e.g., a particularuser in the telepresence session), a data set (e.g., a set of additionalimages obtained of the user in the telepresence session), conditionalsituations, or other variations. In some embodiments, the wearable HMDcan be configured to utilize a plurality of techniques to generatemodels for analysis of the aggregated data. Other techniques may includeusing pre-defined thresholds or data values.

Based on this information and collection of points in the map database,the object recognizers 708 a to 708 n may recognize objects andsupplement objects with semantic information to give life to theobjects. For example, if the object recognizer recognizes a set ofpoints to be a door, the system may attach some semantic information(e.g., the door has a hinge and has a 90 degree movement about thehinge). If the object recognizer recognizes a set of points to be amirror, the system may attach semantic information that the mirror has areflective surface that can reflect images of objects in the room. Thesemantic information can include affordances of the objects as describedherein. For example, the semantic information may include a normal ofthe object. The system can assign a vector whose direction indicates thenormal of the object. Over time the map database grows as the system(which may reside locally or may be accessible through a wirelessnetwork) accumulates more data from the world. Once the objects arerecognized, the information may be transmitted to one or more wearablesystems. For example, the MR environment 700 may include informationabout a scene happening in California. The environment 700 may betransmitted to one or more users in New York. Based on data receivedfrom an FOV camera and other inputs, the object recognizers and othersoftware components can map the points collected from the variousimages, recognize objects etc., such that the scene may be accurately“passed over” to a second user, who may be in a different part of theworld. The environment 700 may also use a topological map forlocalization purposes.

FIG. 8 is a process flow diagram of an example of a method 800 ofrendering virtual content in relation to recognized objects. The method800 describes how a virtual scene may be presented to a user of thewearable system. The user may be geographically remote from the scene.For example, the user may be in New York, but may want to view a scenethat is presently going on in California, or may want to go on a walkwith a friend who resides in California.

At block 810, the wearable system may receive input from the user andother users regarding the environment of the user. This may be achievedthrough various input devices, and knowledge already possessed in themap database. The user's FOV camera, sensors, GPS, eye tracking, etc.,convey information to the system at block 810. The system may determinesparse points based on this information at block 820. The sparse pointsmay be used in determining pose data (e.g., head pose, eye pose, bodypose, or hand gestures) that can be used in displaying and understandingthe orientation and position of various objects in the user'ssurroundings. The object recognizers 708 a-708 n may crawl through thesecollected points and recognize one or more objects using a map databaseat block 830. This information may then be conveyed to the user'sindividual wearable system at block 840, and the desired virtual scenemay be accordingly displayed to the user at block 850. For example, thedesired virtual scene (e.g., user in CA) may be displayed at theappropriate orientation, position, etc., in relation to the variousobjects and other surroundings of the user in New York.

FIG. 9 is a block diagram of another example of a wearable system. Inthis example, the wearable system 900 comprises a map 920, which mayinclude the map database 710 containing map data for the world. The mapmay partly reside locally on the wearable system, and may partly resideat networked storage locations accessible by wired or wireless network(e.g., in a cloud system). A pose process 910 may be executed on thewearable computing architecture (e.g., processing module 260 orcontroller 460) and utilize data from the map 920 to determine positionand orientation of the wearable computing hardware or user. Pose datamay be computed from data collected on the fly as the user isexperiencing the system and operating in the world. The data maycomprise images, data from sensors (such as inertial measurement units,which generally comprise accelerometer and gyroscope components) andsurface information pertinent to objects in the real or virtualenvironment.

A sparse point representation may be the output of a simultaneouslocalization and mapping (e.g., SLAM or vSLAM, referring to aconfiguration wherein the input is images/visual only) process. Thesystem can be configured to not only find out where in the world thevarious components are, but what the world is made of. Pose may be abuilding block that achieves many goals, including populating the mapand using the data from the map.

In one embodiment, a sparse point position may not be completelyadequate on its own, and further information may be needed to produce amultifocal AR, VR, or MR experience. Dense representations, generallyreferring to depth map information, may be utilized to fill this gap atleast in part. Such information may be computed from a process referredto as Stereo 940, wherein depth information is determined using atechnique such as triangulation or time-of-flight sensing. Imageinformation and active patterns (such as infrared patterns created usingactive projectors), images acquired from image cameras, or handgestures/totem 950 may serve as input to the Stereo process 940. Asignificant amount of depth map information may be fused together, andsome of this may be summarized with a surface representation. Forexample, mathematically definable surfaces may be efficient (e.g.,relative to a large point cloud) and digestible inputs to otherprocessing devices like game engines. Thus, the output of the stereoprocess (e.g., a depth map) 940 may be combined in the fusion process930. Pose 910 may be an input to this fusion process 930 as well, andthe output of fusion 930 becomes an input to populating the map process920. Sub-surfaces may connect with each other, such as in topographicalmapping, to form larger surfaces, and the map becomes a large hybrid ofpoints and surfaces.

To resolve various aspects in a mixed reality process 960, variousinputs may be utilized. For example, in the embodiment depicted in FIG.9, Game parameters may be inputs to determine that the user of thesystem is playing a monster battling game with one or more monsters atvarious locations, monsters dying or running away under variousconditions (such as if the user shoots the monster), walls or otherobjects at various locations, and the like. The world map may includeinformation regarding the location of the objects or semanticinformation of the objects and the world map can be another valuableinput to mixed reality. Pose relative to the world becomes an input aswell and plays a key role to almost any interactive system.

Controls or inputs from the user are another input to the wearablesystem 900. As described herein, user inputs can include visual input,gestures, totems, audio input, sensory input, etc. In order to movearound or play a game, for example, the user may need to instruct thewearable system 900 regarding what he or she wants to do. Beyond justmoving oneself in space, there are various forms of user controls thatmay be utilized. In one embodiment, a totem (e.g. a user input device),or an object such as a toy gun may be held by the user and tracked bythe system. The system preferably will be configured to know that theuser is holding the item and understand what kind of interaction theuser is having with the item (e.g., if the totem or object is a gun, thesystem may be configured to understand location and orientation, as wellas whether the user is clicking a trigger or other sensed button orelement which may be equipped with a sensor, such as an IMU, which mayassist in determining what is going on, even when such activity is notwithin the field of view of any of the cameras.)

Hand gesture tracking or recognition may also provide input information.The wearable system 900 may be configured to track and interpret handgestures for button presses, for gesturing left or right, stop, grab,hold, etc. For example, in one configuration, the user may want to flipthrough emails or a calendar in a non-gaming environment, or do a “fistbump” with another person or player. The wearable system 900 may beconfigured to leverage a minimum amount of hand gesture, which may ormay not be dynamic. For example, the gestures may be simple staticgestures like open hand for stop, thumbs up for ok, thumbs down for notok; or a hand flip right, or left, or up/down for directional commands.

Eye tracking is another input (e.g., tracking where the user is lookingto control the display technology to render at a specific depth orrange). In one embodiment, vergence of the eyes may be determined usingtriangulation, and then using a vergence/accommodation model developedfor that particular person, accommodation may be determined. Eyetracking can be performed by the eye camera(s) to determine eye gaze(e.g., direction or orientation of one or both eyes). Other techniquescan be used for eye tracking such as, e.g., measurement of electricalpotentials by electrodes placed near the eye(s) (e.g.,electrooculography).

Speech tracking can be another input can be used alone or in combinationwith other inputs (e.g., totem tracking, eye tracking, gesture tracking,etc.). Speech tracking may include speech recognition, voicerecognition, alone or in combination. The system 900 can include anaudio sensor (e.g., a microphone) that receives an audio stream from theenvironment. The system 900 can incorporate voice recognition technologyto determine who is speaking (e.g., whether the speech is from thewearer of the ARD or another person or voice (e.g., a recorded voicetransmitted by a loudspeaker in the environment)) as well as speechrecognition technology to determine what is being said. The local data &processing module 260 or the remote processing module 270 can processthe audio data from the microphone (or audio data in another stream suchas, e.g., a video stream being watched by the user) to identify contentof the speech by applying various speech recognition algorithms, suchas, e.g., hidden Markov models, dynamic time warping (DTW)-based speechrecognitions, neural networks, deep learning algorithms such as deepfeedforward and recurrent neural networks, end-to-end automatic speechrecognitions, machine learning algorithms (described with reference toFIG. 7), or other algorithms that uses acoustic modeling or languagemodeling, etc.

The local data & processing module 260 or the remote processing module270 can also apply voice recognition algorithms which can identify theidentity of the speaker, such as whether the speaker is the user 210 ofthe wearable system 900 or another person with whom the user isconversing. Some example voice recognition algorithms can includefrequency estimation, hidden Markov models, Gaussian mixture models,pattern matching algorithms, neural networks, matrix representation,Vector Quantization, speaker diarisation, decision trees, and dynamictime warping (DTW) technique. Voice recognition techniques can alsoinclude anti-speaker techniques, such as cohort models, and worldmodels. Spectral features may be used in representing speakercharacteristics. The local data & processing module or the remote dataprocessing module 270 can use various machine learning algorithmsdescribed with reference to FIG. 7 to perform the voice recognition.

With regard to the camera systems, the example wearable system 900 shownin FIG. 9 can include three pairs of cameras: a relative wide FOV orpassive SLAM pair of cameras arranged to the sides of the user's face, adifferent pair of cameras oriented in front of the user to handle thestereo imaging process 940 and also to capture hand gestures andtotem/object tracking in front of the user's face. The FOV cameras andthe pair of cameras for the stereo process 940 may be a part of theoutward-facing imaging system 464 (shown in FIG. 4). The wearable system900 can include eye tracking cameras (which may be a part of aninward-facing imaging system 462 shown in FIG. 4) oriented toward theeyes of the user in order to triangulate eye vectors and otherinformation. The wearable system 900 may also comprise one or moretextured light projectors (such as infrared (IR) projectors) to injecttexture into a scene.

FIG. 10 is a process flow diagram of an example of a method 1000 forinteracting with a virtual user interface. The method 1000 may beperformed by the wearable system described herein. The method 1000 mayperform the method 1000 in a telepresence session.

At block 1010, the wearable system may identify a particular UI. Thetype of UI may be predetermined by the user. The wearable system mayidentify that a particular UI needs to be populated based on a userinput (e.g., gesture, visual data, audio data, sensory data, directcommand, etc.). The UI may be specific to a telepresence session. Atblock 1020, the wearable system may generate data for the virtual UI.For example, data associated with the confines, general structure, shapeof the UI etc., may be generated. In addition, the wearable system maydetermine map coordinates of the user's physical location so that thewearable system can display the UI in relation to the user's physicallocation. For example, if the UI is body centric, the wearable systemmay determine the coordinates of the user's physical stance, head pose,or eye pose such that a ring UI can be displayed around the user or aplanar UI can be displayed on a wall or in front of the user. In thetelepresence context, the UI may be displayed as if the UI weresurrounding user to create a tangible sense of another user's presencein the environment (e.g., the UI can display virtual avatars of theparticipants around the user). If the UI is hand centric, the mapcoordinates of the user's hands may be determined. These map points maybe derived through data received through the FOV cameras, sensory input,or any other type of collected data.

At block 1030, the wearable system may send the data to the display fromthe cloud or the data may be sent from a local database to the displaycomponents. At block 1040, the UI is displayed to the user based on thesent data. For example, a light field display can project the virtual UIinto one or both of the user's eyes. Once the virtual UI has beencreated, the wearable system may simply wait for a command from the userto generate more virtual content on the virtual UI at block 1050. Forexample, the UI may be a body centric ring around the user's body or thebody of a person in the user's environment (e.g., a traveler). Thewearable system may then wait for the command (a gesture, a head or eyemovement, voice command, input from a user input device, etc.), and ifit is recognized (block 1060), virtual content associated with thecommand may be displayed to the user (block 1070).

Examples of a Wearable Device and Imaging a User's Face

FIG. 11 illustrates an example wearable device which can acquire imagesof the user's face. The wearable device 1150 can be an examplehead-mounted device (HMD) as described with reference to FIG. 2. Thewearable device 1150 may be a SAR device which may include ahead-mounted display for rendering virtual objects from the perspectivesof the user's eyes. The images acquired by the wearable device caninclude still images, animations, individual frames from a video, or avideo.

The wearable device 1150 can include an imaging system 1160 which can beconfigured to image the user's 210 face. The imaging system 1160 may bean example of the inward-facing imaging system 462 shown in FIG. 4. Forexample, the imaging system 1160 may include sensors such as eye cameras(eye camera 1160 a and eye camera 1160 b) configured to image theperiocular region of the user's eyes 1110 while the user 210 is wearingthe wearable device 1150. In this example, the eye 1110 b can correspondto the eye 302 and the eye 1110 a can correspond to the eye 304 shown inFIG. 3. The wearable device 1150 can also include other types of sensorssuch as, e.g., inertial measurement units, pressure sensors, proximitysensors, etc. One or more of these sensors can be disposed on the frameof the wearable device 1150 (e.g., on one or both ear stem). Dataacquired by the sensors may be used to determine the relative positionbetween the wearable device 1150 and user's face.

Each eye camera may have a field-of-view (FOV). For example, the FOV forthe eye camera 1160 a can include the region 1120 a and the region 1130.The FOV for the eye camera 1160 b can include the region 1120 b and theregion 1130. The FOV of the eye camera 1160 a and the FOV of the eyecamera 1160 b may overlap at the region 1130.

As shown in FIG. 11, the imaging system 1160 points toward the head ofthe user 210. The eye camera 1160 a may be configured to image the eye1110 a while the eye camera 1160 b may be configured to image the eye1110 b. In this figure, the optical axis 1140 a of the eye camera 1160 ais parallel to the optical axis 1140 b of the eye camera 1160 b.

In some implementations, one or both of the eye cameras may be rotatedsuch that the optical axes of the two eye cameras are no longer inparallel. For example, the two eye cameras may point slightly towardseach other (e.g., particularly if the eye cameras are disposed nearoutside edges of the frame of the device 1150). This implementation maybe advantageous because it can create a cross eyed configuration whichcan increase the overlap of the FOV between the two cameras as well asto allow the two eye cameras to image the face at a closer distance.

When the wearable device 1150 is too close to the user 210, the eyecameras may be out of focus. For example, assuming the periocularseparation (e.g., a distance between periocular features on the left andright side of the face) for the user is 46 mm (typical for an adultmale) and each of the two eye cameras has a horizontal FOV of 66 degrees(appropriate for eye-tracking), then the wearable device may takepictures when the distance between the face and the wearable device isat least about 175 mm. The minimum focal distance for the lenses of manyeye cameras is approximately 14 mm. If the lenses have fixed focallength, their depth of focus needs to be about 65 diopters.

If the images are obtained when there is insufficient depth of focus,the wearable device 1150 may treat the images as low resolution images.As a result, the face model generated by the wearable device may have alower fidelity or have sparse representations of gross facial features.Such face model may still be used to deduce an interocular separationfor the user (e.g., an interpupillary distance), which is useful fordetermining whether the wearable device fits the user's face.

Although the example described in FIG. 11 illustrates two eye cameras,wearable device 1150 is not required to have two eye cameras. In someembodiments, the imaging system 1160 may include one eye camera imagingthe user's face. The one eye camera may be configured to image theperiocular region associated with one eye or the periocular regions forboth eyes. In other embodiments, the wearable device 1150 may includemore than two eye cameras.

The wearable device 1150 can build a model of the user's face using theimages of the user's face acquired by the imaging system 1160. Theimages may be acquired by the imaging system 1160 when the user isputting on or taking off the device. The images may also be acquired byscanning the user's face using the outward-facing imaging system 464(shown in FIG. 4). For example, to scan the user's face using theoutward-facing imaging system 464, the user may turn the wearable device1150 such that the outward-facing imaging system 464 is facing towardthe user's face (rather than the user's environment). The wearabledevice can create a model of the user's face during an initializationphase of the wearable device, such as, e.g., when the user first usesthe wearable device, or when a user turns on the wearable device.Examples of generating a face model using images acquired by the imagingsystem 1160 are also described in U.S. Provisional Application No.62/400,907, titled “FACE MODEL CAPTURE BY AN AUGMENTED REALITY DEVICE,”the disclosure of which is hereby incorporated by reference herein inits entirety.

The model of the user's face may be generated based on a base model anddata specific to a user. For example, the wearable device may use a basemodel pre-generated from data associated with a group of people andcustomize the base model based on user specific information obtained byanalyzing the images acquired by the wearable device. In someimplementations, the base model may be associated with a group of peoplehaving similar demographic information to the user of the wearabledevice. For example, if the user is a female teenager, the wearabledevice may access a base model associated with a typical femaleteenager. As another example, if the user belongs to certain genderand/or race group, the wearable device may access a base model common tothat gender and/or race group. The wearable device can also determine alikelihood of a location of a certain facial feature on the map based onstatistical analysis on images associated with a group of people or theuser. The wearable device can then update the likelihood or confirm thelocation of the periocular feature based on images acquired specific tothe user.

In addition to or in alternative to identifying the presence ofperiocular features in an image, the wearable device can analyze imagesacquired by the inward-facing imaging system to determine the relativeposition between the wearable device and the user. The eye cameras ofthe inward-facing imaging system 462 (shown in FIG. 4) can continuouslyobtain images within their FOV. The eye cameras may also be configuredto only acquire images based on a trigger. For example, the eye camerasmay be triggered to capture one or more images when the user is puttingon the wearable device (e.g., as determined by a movement of thewearable device based on the IMU). Alternatively, the eye cameras maycapture images at a selected frequency. The frequency may be any desiredtime interval, such as every few seconds or minutes, and the frequencymay change depending on requirements of the system using the images.

The wearable device can also build the face model based on the userspecific images. For example, the wearable device may generate a modelof the user's face solely from the images acquired by the inward-facingimaging system or by the outward-facing imaging system. In someimplementations, the wearable device may update the user's face model asmore images of the user's face are acquired. For example, the wearabledevice may generate a face model based on the images acquired by theinward-facing imaging system as the user is putting on the device. Thewearable device can update the face model based on new images acquiredwhen the user is taking off the device or in the next session where theuser is putting on the device again.

Although these examples refer to building a face model or creating a mapof a user's face using a wearable device, some embodiments may includethe wearable device communicating with a remote computing device togenerate or otherwise obtain a face model. For example, the wearabledevice can acquire images of the user's face and pass the images (aloneor in combination with other information of the user, such as, e.g., theuser's demographic information) to a remote computing device (e.g., suchas a server). The remote computing device can analyze the images andcreate the face model and pass the face model to the wearable device ofthe user or pass the face model to another user's wearable device (e.g.,during a telepresence session).

Further, in addition to or in alternative to determining fit or removalof the wearable device, or adjusting a rendering location of virtualimages, the face model can also be used to perform user identification.As an example of determining a user's identity based on the images, thewearable device can analyze facial features of the user by applyingvarious facial recognition algorithms to the acquired images (e.g., faceshape, skin tone, characteristics of nose, eyes, cheeks, etc.). Someexample facial recognition algorithms include principal componentanalysis using eigenfaces, linear discriminant analysis, elastic bunchgraph matching using the Fisherface algorithm, the hidden Markov model,the multilinear subspace learning using tensor representation, and theneuronal motivated dynamic link matching, or a 3D face recognitionalgorithm. The device may also analyze the images to identify the irisand determine a biometric signature (e.g., an iris code), which isunique to each individual.

The wearable device can also perform image registration based on theimages acquired by the wearable device while the device is being put onor taken off the user's face. The resulting image obtained from theimage registration can include a portion of the user's environment(e.g., the user's room or another person near the user) in addition toor in alternative to the user's face.

Examples of Imaging a Periocular Region

As described with reference to FIG. 11, the images acquired by theimaging system 1160 may include a portion of the periocular region ofthe user. The periocular region can include one or more periocularfeature, or portions of periocular features. Periocular features mayinclude, for example, an eye, an eye socket, an eyebrow, a nose, acheek, or a forehead. Other features or user-specific details of theface may also be considered periocular features.

FIG. 12A illustrates an example image 1200 a of a periocular region 1270for one eye, such as could be obtained from an HMD camera imaging theperiocular region 1270 of a user. In this example, the periocular region1270 includes periocular features such as an eye 1210 a, an eye socket),eyebrow 1220 a, portions of the nose 1230 a, cheek 1240 a, and forehead1250 a. Each periocular feature may have a variety of characteristicsassociated with the periocular feature. The characteristics may bespecific to each different periocular feature. For example, theperiocular feature eyebrow 1220 a may have characteristics including theshape of the eyebrows, the color of the eyebrow, likely movements ormovement directions of the eyebrow, etc. The periocular feature eye 1210a may have characteristics such as, for example, shape, size, locationof eye corners, gaze direction, pupil location, location of eyeballcenter, shape and folds of the eyelid, texture of skin around theeyeball, and so forth. Many other characteristics may also be used toidentify and track each periocular feature. One or more characteristicof one or more periocular feature may be represented by keypoints, pointclouds, or other types of mathematical representations.

The wearable device can compute and track periocular features andassociated characteristics using neural network or visual keypointstechniques such as scale-invariant feature transform (SIFT), speeded uprobust features (SURF), oriented FAST and rotated BRIEF (ORB), binaryrobust invariant scalable keypoints (BRISK), fast retina keypoint(FREAK), etc. In some embodiments, a particular facial feature may betracked using a detector specifically designed for that particularperiocular feature. For example, periocular feature characteristics,such as eye corners, nose features, mouth corners, etc., may beidentified and tracked separately using various algorithms. Tracking oneor more of these periocular feature characteristics separately may beadvantageous because each periocular feature and/or characteristic maybe prone to substantial motion while the user making facial expressionsor is speaking. The algorithms used to track these periocular featuresand characteristics may take into account the range of mobility. As anexample, some periocular features and/or associated characteristics maybe likely to move in certain directions and/or may be likely to remainmore stable in other directions (e.g., eyebrows tend to move up or downbut not left or right).

The wearable device can analyze the movements of the periocular featuresstatistically. These statistics may be used to determine the likelihoodthat the facial features will move in a certain direction. In someembodiments, one or more periocular features or characteristics may beremoved or untracked to reduce processing demand or to improvereliability. In the instance where it is desired to improve reliability,it may be advantageous to ignore or mask periocular features orcharacteristics that are more error prone than others. For example, insome embodiments as described with reference to FIG. 12B, the wearabledevice may ignore pixels in a center area 1212 of the eye 1210 b so thateye movement is not recognized by the HMD when tracking other periocularfeatures or characteristics in the periocular region 1270.

The wearable device can also use visual simultaneous location andmapping (vSLAM) techniques, such as sequential Bayesian estimator (e.g.,Kalman filter, extended Kalman filter, etc.), bundle adjustment, etc.,to identify and track periocular features and characteristics. In someembodiments, the wearable device may be configured to allow depthperceptions and mapping of the user. For example, the wearable devicecan construct a dense map, which encodes at least a portion of the face,from data acquired by one or more cameras. In contrast with a keypointmap, the dense map may comprise patches or regions of the face whose 3Dshape is measured. The patches or the regions may be used to compute thelocation of the HMD relative to the face of the user using techniquessuch as iterative closest algorithm or similar algorithms.

The size and content within the periocular region captured by a cameraon the wearable device may depend on the eye camera's FOV. In someimplementations, the eye camera may not have a large FOV to fit allrecognizable periocular features within the captured periocular region.For example, the images captured by the eye camera may include the eyesocket but not the eyebrow. Technical specifications of the camera maydetermine which periocular features are most likely to remain present inmultiple captured frames of a periocular region and which periocularfeatures are most reliable for tracking.

As described with reference to FIG. 11, in some situations, althougheach eye camera is configured to image an eye, the two eye cameras (onefor the left eye and one for the right eye) may have an overlapping FOV1130 such that overlapping periocular regions are imaged by the cameras.This may be because the FOV of the two cameras is sufficiently wide, thecameras are angled inwardly toward a center of a user's face, thecameras are positioned near each other, and/or because the two camerasare sufficiently far away from the user. As a result, a portion of theuser's face, typically a center portion (e.g., nose), may be captured byboth eye cameras. The wearable device may combine the images obtainedfrom the two cameras, determine whether the combined image includesperiocular features, and if periocular features are determined to bepresent within the images, the wearable device may identify theperiocular features.

In some implementations, images acquired by eye cameras may be lowresolution images because the eye cameras may be out of focus. Out offocus or low resolution images may result from physical limitationswithin the hardware of the wearable or improper positioning or movementof the wearable device. For example, out of focus images may be causedby eye cameras being too close or too far from the user's face.Alternatively, in some embodiments, it may be desired to capture lowerresolution images. For example, the wearable device may not need highquality images to track the periocular features (e.g., for determiningrelative position between the user's face and the wearable device) andthe use of high resolution images may place more demand on software andhardware systems of the wearable device without providing a usefulimprovement in output. In order to minimize demand on the wearabledevice in terms of processing time, sampling frequency, powerconsumption, and other metrics, the resolution of the images obtainedfrom an eye imager may be down-sampled relative to their originalresolution or the resolution used in other applications (e.g.,eye-tracking) to a minimum resolution necessary for detecting andidentifying periocular features. For example, the eye cameras may imagethe user's eyes for the purpose of tracking the user's direction ofgaze. The images obtained by the eye cameras can be downsized by thewearable device for use in determining the relative position between theuser's face and the wearable device. This implementation may beadvantageous because the wearable device may not need detailed,high-resolution information of the periocular region to determine therelative position.

In some situations, the wearable device can dynamically change theresolution of the eye camera. The resolution of the eye camera may beselected based on timing, device position with respect to a user's eyes,or intended use of the captured images. For example, it may beadvantageous to capture images of a user's face from a distance furtheraway than a normal resting use position so that a larger portion of theuser's face is imaged for use is constructing a model of the user'sface. It may be determined that these images are best captured as theuser is putting on the wearable device. The resolution of the eye cameramay be set to a high resolution when the user is putting on the wearabledevice so that high resolution photos of the user's entire face areavailable for use in generating a model of the user's face. While thewearable device is on the user, the resolution of the eye camera may beset to a low resolution so that the eye camera can continuously testwhether the wearable device is in place without slowing down otherprocessing applications. In various embodiments, the low resolution maybe a factor smaller than the high resolution, where the factor is lessthan one, e.g., 0.5, 0.25, 0.1, or less.

Examples of Masking Portions of a Periocular Region

FIG. 12B illustrates an example image of periocular region 1270, where aportion of the periocular region in the image is masked out by thewearable device. In this example, the eye camera acquires an image 1200b of the periocular region 1270. The image 1200 b shows that theperiocular region 1270 can include the eyebrow 1220 b, the eye 1210 b,and portions of the nose 1230 b, cheek 1240 b, and forehead 1250 b.

A portion of the image 1200 b of the periocular region may be masked(such as, e.g., being ignored or otherwise excluded from image analysis)to reduce variations arising from a biological state of an eye (such aschanges in eye pose, pupil dilation, blink, etc.). Characteristics ofthe eye, such as eye color, position of eyeball, and so forth, may alsobe highly variable among different people. These variations, incombination with variables relating to biological state of the eye, mayintroduce noise and error as the wearable device is determining whetherthe position of the wearable device has changed relative to the user'seye. Thus, masking the highly variable portion of the periocular regionbeing imaged may reduce error and may also reduce the amount ofcomputation needed to analyze the image. For example, as shown in FIG.12, a center area 1212 of the eye 1210 b shown in periocular region 1270may be masked so that it is ignored during image analysis. In someembodiments, the center area 1212 includes the iris and/or sclera of theeye. As a result, the wearable device will not analyze information inthe center area 1212 of the perioculus while analyzing the image 1200 bof the periocular region surrounding the ignored pixels in the area1212. Center area 1212 may be automatically defined and bounded usingperiocular features or characteristics of periocular features.

Specular reflections occurring on the exposed portions of the eyeball,can also be masked. This implementation is particularly advantageous forimproving accuracy when determining the relative position between theuser's face and the wearable device. As the user moves around in theenvironment, specular reflections from the user's eye may change basedon biological factors, such as where the user is looking, and may alsochange based on external factors, such as what the user is currentlyseeing, changes in environmental light sources, changes in distances tolight sources, etc. However, changes in specular reflection maysometimes, but not always, be attributed to a change in the relativeposition between the user's face and the wearable device. Thus, it maybe advantageous to ignore (or not analyze) this information since it maynot be reliable for the purpose of determining relative position betweena user's eye and the wearable device.

Examples of Identifying Periocular Features

The wearable device can use images acquired by the eye cameras to traina machine learning model to identify periocular features in theperiocular region. The wearable device may also use the objectrecognizers 708 (described in FIG. 7) to perform the identification. Theobject recognizers 708 may implement the machine learning model trainedfrom the images acquired by the eye cameras. The periocular region maybe associated with one or both eyes. The machine learning model may betrained using periocular features, or characteristics associated withperiocular features, generic to a group of people or specific to anindividual. For example, the wearable device can train the machinelearning model based on the characteristics of the periocular featuressuch as a user's eyebrows and eye socket. As another example, thewearable device can train the machine learning model using theperiocular features and/or associated characteristics of periocularfeatures of other people who have the same or similar ethnicity andgender as the user.

The detection and identification of periocular features may be performedautomatically using neural network techniques (such as sparseauto-encoder or similar clustering techniques or deep neural networksusing many hidden layers) or other machine learning algorithms. In someimplementations, the machine learning model may be customized based onits application. For example, if the machine learning model is used fordetermining whether the wearable device fits the user's face, themachine learning model may be trained to identify detailedcharacteristics of periocular features such as the location of eyebrowsand eye balls. As another example, if the machine learning model is usedfor detecting whether the user has removed the wearable device, themachine learning model may not need to learn the detailedcharacteristics of periocular features of the user's face. Rather, itmay be sufficient to identify a minimum set of periocular features suchas the eye socket and the nose of the user.

Examples of Determining Relative Position Between the HMD and the User'sFace

The wearable device can identify periocular features in the periocularregion in an image captured by cameras on the wearable device and mayuse the identified periocular features, and characteristics thereof, todetermine a relative position between the wearable device and the user'sface. In certain embodiments, the wearable device can calculate therelative position between the wearable device and the user separatelyfor each eye. For example, when the wearable device has two eye cameras,each configured to image one periocular region of the user, the wearabledevice may calculate one relative position between the left eye and theleft eye camera and another relative position between the right eye andthe right eye camera. Relative positions between the left eye and thewearable device and between the right eye and the wearable device maythen be calculated. In some embodiments, calculating distances betweeneyes and the wearable device may also depend on known geometricinformation about positions of eye cameras on the wearable in additionto known technical details about the cameras themselves, such as fieldof view, focal length, etc.

While the wearable device may track the relative positions forrespective eyes separately, the wearable device may also be configuredto combine relative position information between both eyes and thewearable device. Alternatively, a wearable device may include one eyecamera capable of imaging both the user's left and right eyessimultaneously. In other embodiments, a single eye camera on thewearable device may image a periocular region of only one eye, fromwhich relative positional data of the HMD with respect to the user maybe extrapolated. More or fewer than two cameras may be used to image oneor more periocular regions of a user and that the number of cameras usedmay depend upon the technical specifications of the camera and thedesired types and number of images needed for a particular applicationor tracking algorithm.

As further described herein, the relative positions between the user'sface and the wearable device can be used to determine whether apositional shift has occurred between the wearable device and the user.In some embodiments, detection of a positional shift may cause a displayof the wearable device to adjust rendering locations of virtual objectsso that the rendered virtual content may align correctly with the user'seyes. Because the relative position between the left eye and thewearable device may be different from the relative position between theright eye and the wearable device (such as when the wearable devicetilts to one side), the adjustment to the rendering location of avirtual object may be different for the left eye display and the righteye display.

FIGS. 13A-13C illustrate examples of periocular regions from a wearabledevice having various example relative positions with respect to theface of the user. The wearable device may be an HMD. FIG. 13Aillustrates an example where the HMD (not pictured) is at its normalresting position with respect to the user's face, as indicated by areference line 1314 of HMD aligning with left and right pupil centers1318 a, 1318 b. FIG. 13B illustrates an example where the HMD is tiltedto one side as compared with the normal resting position of FIG. 13A.FIG. 13C illustrates an example where the HMD has tilted or shiftedforward (e.g., the HMD has slid down the user's nose) as compared withthe normal resting position of FIG. 13A. In these example figures, theuser 1310 is wearing an HMD which has at least two eye cameras to imageperiocular regions 1312 a, 1312 b. As shown in FIG. 13A, one eye camerais configured to image the periocular region 1312 a while the other eyecamera is configured to image the periocular region 1312 b; however,more or fewer eye cameras may be used to capture one or more periocularregions of the user. For example, a single eye camera having sufficientfield of view may image both periocular regions 1312 a, 1312 b. In theseexamples, the normal resting position is associated with the HMD. Insome implementations, the normal resting position may be associated withthe user's eye.

The wearable device can analyze the images obtained by one or both eyecameras to determine the relative position between the HMD and the user.The HMD can determine a normal resting position of the HMD and determinethe relative position of the HMD with respect to a user based on apositional deviation from the normal resting position. The normalresting position of the HMD may be determined and calibrated during theinitialization phase of the wearable device. For example, when a userfirst uses the wearable device, the wearable device may build a facemodel (e.g., a map of the user's face) and determine the normal restingposition of the HMD based on the face model. As further described withreference to FIGS. 14A and 14B, when the HMD is at the normal restingposition, the HMD may not need to adjust the rendering location of thevirtual objects. Further, the HMD can determine that it fits the user'sface if the HMD is at the normal resting position (see, e.g., FIG. 13A).The HMD can determine one or more goodness of fit parameters (furtherdescribed below) that can be used to automatically assess the fit of theHMD on the user's face. Goodness of fit parameters can include one ormore of, e.g., relative distance between the HMD and the user's eyes,amount of tilt or shift of the HMD on the user's face, interpupillarydistance (IPD), locations of centers of pupils relative to the display,position of a reference line of the HMD relative to the pupils, etc.

While the user is using the wearable device, the wearable device cankeep tracking the relative position between the HMD and the user using avariety of techniques. For example, the wearable device can identify andtrack visual keypoints associated with periocular features. Movement ofvisual keypoints associated with periocular features may indicaterelative motion of the HMD with respect to the user's eyes and face. Insome embodiments, the wearable device can also match a region of theface as identified in the acquired images relative to a dense map of theuser's face to compute the location of the HMD relative to the face. Asanother example, the HMD may detect or calculate a distance from the HMD(or a component of the HMD such as an eye camera) to the eyes of user1310. If the distance of the HMD passes a certain distance threshold(e.g., when the HMD is too close or too far), the HMD may determine thatthe HMD does not fit the user 1310 very well and may determine that therendering locations of the pixels need to be adjusted. On the otherhand, if the detected or calculated distance between the HMD and theuser's eyes falls within a threshold range, the wearable device maydetermine that the HMD fits the user acceptably and the pixels will notneed to be adjusted.

It may be that the HMD shifts asymmetrically with respect to the user'seyes. For example, the HMD may tilt to one side as shown in FIG. 13B. Insuch a position, a distance detected or calculated between the HMD andthe left eye may differ from a distance detected or calculated betweenthe HMD and the right eye. For example, as shown in FIG. 13B, thedistance between the user's right eye 1324 b and the HMD may be smallerthan the distance between the user's left eye 1324 a and the HMD. TheHMD may use this difference as a cue to calculate in which direction theHMD is tilted and/or to calculate the degree of tilt. The direction anddegree of tilt may be used to determine a direction and magnitude ofrender location adjustment necessary to accommodate for the tilt of theHMD with respect to the user's eyes.

As another example, the HMD may use IPD as one of the parameters fordetermining fit of the HMD and/or location of the HMD with respect tothe user's eyes. The HMD may be capable of detecting and/or calculatinga user's IPD based on images obtained from eye cameras. In someembodiments, knowledge of geometric placement of eye cameras on the HMD,specifics about orientation of the eye cameras, and information aboutcamera field of view, focal distance, and other technical details mayalso be used in calculating user IPD.

The HMD may obtain an acceptable IPD range for the user 1310 (e.g., byaccessing a database storing acceptable values of interpupillarydistances). The acceptable interpupillary distance may be a distance orrange of distances determined specifically for the user 1310 or may bedetermined based on data from a group of people. The HMD can compare theuser's IPD to the acceptable IPD range. If the discrepancy between theuser's IPD 1316 and the acceptable interpupillary distance passes athreshold, the HMD may determine that the HMD does not fit the user verywell. On the other hand, if the discrepancy is within an acceptablerange, the HMD may determine that the HMD fits the user adequately. Incases where the HMD fit is determined to be acceptable, no renderingadjustment is necessary; however, in cases where the HMD fit isinadequate, the HMD may adjust rendering to accommodate for a suboptimalfit.

For example, the interpupillary distance for a typical adult male may bearound 65 mm. The acceptable IPD value may be based on an average valuefor a user of particular age, gender, and/or race. For example, the HMDmay obtain user information indicating that the user is an adult male.This information may be used to obtain an acceptable IPD value for anadult male user, such as 65 mm. In some embodiments, the HMD maycalculate an acceptable IPD range based on the acceptable IPD value fora given user. Continuing the example above, an acceptable IPD range maybe the acceptable IPD value (65 mm) plus or minus a selected distance orpercentage of the acceptable IPD value. The selected distance may be,for example, plus or minus 5 mm or plus or minus 10 mm to giveacceptable IPD ranges of 60 mm-70 mm and 55 mm-75 mm, respectively. Thepercentage of IPD may be, for example, plus or minus 5% or plus or minus10% to give acceptable IPD ranges of 61.75 mm-68.25 mm and 58.5 mm-71.5mm, respectively. Any distance or percentage value may be selected fordetermining an acceptable IPD range.

In some implementations, the interpupillary distance may be calculatedfrom a sample group of people. For example, the HMD can calculate theaverage, mean, or median value (or other statistical values) of theinterpupillary distance for a group of people to be used for thedetermining an acceptable IPD value or range. The sample may take intoaccount the characteristics of the user such as the user's gender, race,age, and so on. For example, if the user of the HMD is a femaleteenager, the HMD may calculate the threshold interpupillary distancefor the user based on data from a group of female teenagers. In additionto or in alternative to interpapillary distance, the HMD can alsocalculate the threshold value based on other parameters, such as theperiocular separation.

The wearable device can also use the detected location of relativecenters of the pupils (1318 a and 1318 b) with respect to the display todetermine the relative position between the HMD and the face. In FIG.13A, the center of the display is shown by reference line 1314. As shownin FIG. 13A, the reference line 1314 of the display aligns with thecenters of the pupils 1318 a and 1318 b. In this example, the HMD maydetermine that the HMD fits the user if the center of the display alignswith the center of the pupils 1318 a and 1318 b. The HMD can furtherdetermine that alignment between the pupils 1318 a, 1318 b and thereference line 1314 of the HMD is correct and that no adjustment to therendering locations of the virtual objects is needed. However, in FIG.13B, the HMD is tilted to one side and the center 1314 of the displaydoes not align with both pupils 1318 a, 1318 b. As another example, inFIG. 13C, the HMD is titled forward or shifted downward, and as aresult, the reference line 1314 of the display does not match thecenters of the pupils 1318 a and 1318 b. In either or both situations,the HMD may send a signal indicating the relative position between theHMD and the user. The signal can cause the HMD to adjust the renderinglocations of the virtual objects. In some embodiments, the HMD mayprovide an alert to display an indication of a goodness of fit to theuser. The indication of goodness of fit displayed to the user may informthe user how to adjust the HMD with respect to pupils 1318 a, 1318 bsuch that alignment between reference line 1314 and pupils 1318 a, 1318b is improved.

In another example, eye cameras may specifically track eyeball center asan indicator of relative position between the HMD and a user. Eyeballcenter location may allow the wearable device to determine relativetilt, rotation, and translation occurring up to three dimensions (e.g.,x-, y-, and z-dimensions or yaw, pitch, and roll angular dimensions). Toreduce errors associated with specular reflection, eye tracking cameraswith specific componentry may be used. For example, eye cameras mayinclude infrared (IR) light emitting diode (LED) lights. Operatingparameters of the eye cameras and/or IR LED lights may also be optimizedto reduce or minimize tracking error caused by specular reflection. Forexample, the IR LED lights may be operated to burst light of aparticular wavelength with relatively high power. An optimized IRwavelength may be between about 800 nm and 900 nm, and an optimizedpower level may correspond to an operating current of between about 2.5mA and 50 mA. Exposure time of the eye cameras may additionally oralternatively be adjusted to reduce error. An optimal exposure time maybe between about 400 microseconds and 8 ms. Additionally oralternatively to adjusting operating parameters of eye cameras,filtering steps may be performed to reduce error caused by reflection.Using one or more of these improvements may allow the wearable device totrack wearable device location with respect to eyeball center with morestability and reliability than a system tracking relative position basedon other periocular features. This may be especially relevant when otherperiocular features are difficult for the wearable device to identifyand track, such as when makeup covers periocular features such aseyelid, eye corners, eyelash length, etc.

In some embodiments, the HMD can use observed asymmetries in the imagesof the periocular region to determine the relative position between theuser's eyes and the wearable device. For example, in FIG. 13A, the HMDmay determine, from the images of the periocular region, that the user'seyes are symmetric, and accordingly determine that the HMD is at thenormal resting position. In some embodiments, other periocular featuresmay be used to determine symmetry between the two periocular regions. InFIG. 13B, however, the periocular region 1322 b and 1322 a observed inthe images may not have the same periocular features. In someembodiments, while the same periocular features may be present in eachof the periocular regions 1322 a, 1322 b, their locations or sizeswithin the captured image may vary. For example, the periocular region1322 b may include a larger portion of the forehead than the periocularregion 1322 a, while the periocular region 1322 a may include a largerportion of the cheek than the periocular region 1322 b. As a result, theHMD may determine that the HMD is tilted with respect to its normalresting position.

Although the two eye cameras for imaging the periocular regions 1312 aand 1312 b do not have an overlapping FOV in the examples shown in FIGS.13A-13C, in some embodiments, the two eye cameras may have anoverlapping FOV. As a result, a portion of the region 1312 a may overlapwith a portion of the region 1312 b. This overlapping FOV may be usefulfor determining the relative position between the HMD and the face. Forexample, if the HMD is at its normal resting position relative to theuser, the overlapping FOV may include a top portion of the nose.However, where the image in the overlapping FOV includes a portion ofone eye (instead of the top portion of the nose), the HMD may determinethat the HMD is titled. As another example, if the image includes alarge portion of the nose, the HMD may determine that it has slid downthe user's nose. Thus, the presence or absence of periocular features ineach periocular region or in an overlapped periocular region may providean indication of relative position of the HMD with respect to the user'seyes and face.

These example factors may be used alone or in combination to determinethe relative position between the HMD and the user's face. For example,although the HMD detects asymmetries in the wearer's eyes in the images,the HMD may nevertheless determine that it is at the normal restingposition because the relative centers of the pupils do not indicate atilt. Thus, the HMD may be capable of performing more than one check todetermine position of the HMD so that false indications of anHMD-to-user misalignment do not incorrectly and unnecessarily trigger anadjustment in render location.

The HMD can determine the relative position using a variety ofalgorithms For example, the wearable system can track visual keypointsand relative positions using SIFT, SURF, ORB, FREAK, BRISK. V-SLAMtechniques, such as the use a sequential Bayesian estimator (e.g. KalmanFilter, Extended Kalman Filter, etc.) or Bundle Adjustment may also beemployed. Where the cameras are capable (either singly or by integratingdata from multiple cameras) of depth perception (either by stereoscopy,structured light projection, time of flight measurement, or any othermeans), a dense map may be constructed of the whole or parts of theface. Such a dense map can comprise of patches or regions whose threedimensional shape is measured. Such regions may be used, by matching(e.g. with the Iterative Closest Point algorithm, or similar), tocompute the location of the HMD relative to the face. The HMD can use amodel of the face (e.g., built during the initialization phase of theHMD) to determine the relative position between the HMD and the user'sface.

In addition to or in alternative to determining the relative positionbetween the HMD and the user's face, the wearable device can alsodetermine a pose of the HMD. The pose may be with respect to the user'sface, such as titling to one side or forward by a certain degree ordistance, or rotated in a clockwise or counterclockwise direction aboutthe user's head, etc. The wearable device can determine the pose of theHMD using the information on the relative position between the HMD andthe user's face. The wearable device can also determine the pose of theHMD directly from the images acquired by one or more cameras of thewearable device.

Adjusting a Rendering Location of a Virtual Object

The relative position between the HMD and the user's face or the HMD'spose may be used to compute an adjustment to a rendering location of thevirtual objects.

FIGS. 14A and 14B illustrate an example of adjusting a renderinglocation of a virtual object in a HMD. In these examples, the HMD can bea SAR display. In FIG. 14A, the eye 1410 is associated with an eyecoordinate system 1412 and the HMD 1420 is associated with a renderingcoordinate system 1422. In this example, the u-axis in the eyecoordinate system 1412 corresponds to the x-axis in the renderingcoordinate system 1422, the v-axis in the eye coordinate system 1412corresponds to the y-axis in the rendering coordinate system 1422, andthe w-axis in the eye coordinate system 1412 corresponds to the w axisin the rendering coordinate system 1422. Another example of the x-y-zcoordinate of the rendering coordinate system is shown in FIG. 6.Although in these examples, the coordinate systems are illustrated usinga Cartesian Coordinate system, other types of coordinate systems, suchas, e.g., a Polar Coordinate system, may also be used with thetechniques described herein.

In FIG. 14A, when the eye is at position 1414 a, the user can perceive avirtual object 1430 at a position 1424 a (represented by the letter “p”)in the rendering coordinate system 1422. The position 1414 a mayrepresent the normal resting position of the eye 1410 (with respect tothe HMD 1420) or the HMD 1420. In some implementations, thecorresponding position 1424 a for the wearable device 1420 may also bereferred to as normal rendering position of the virtual object. In FIG.14B, the eye deviates from its normal resting position by moving alongthe u axis in the eye coordinate system 1412 from the position 1414 a tothe position 1414 b. This deviation may be a result of HMD's movement.For example, when the HMD may tilt forward or move to one side, therelative position between the HMD and the user's eye may change. Thewearable device can similarly dynamically update the rendering locationsof the virtual objects based on the tilting of the HMD. As an example,the HMD 1420 may shift the rendering location of the virtual object 1430along the x-axis in the rendering coordinate system 1422 from theposition p 1424 a to the position p* 1424 b. The shift can cause thevirtual object 1430 to appear in the same location in the user'senvironment before and after the eye's 1410 movement in FIGS. 14A and14B. Accordingly, the user will not perceive that the HMD has shifted ormoved slightly on the user's head since rendering of the virtual objectsis dynamically adjusted to correct for HMD movement, thereby providingan improved user experience.

As described with reference to FIG. 2, an SAR system can render virtualobjects from the perspective of the user's eye. The rendering viewpointassociated with the user's eye may be represented by a virtual camera ina rendering system, such as the virtual camera in OpenGL or DirectX. Toadjust the rendering locations of virtual objects, the virtual cameramay be displaced or rotated based on a displacement of the normalresting position of the user's eye (with respect to the HMD) or the HMD.The amount of adjustment may be a fraction of change of the relativeposition between the user's face and the HMD. For example, thedisplacement of the virtual camera may be a ratio (such as 0.2, 0.4,0.6, etc.) of the movement of the HMD or the user's eye.

As an example, as shown in FIGS. 14A and 14B, the wearable device canupdate the position of the virtual camera from position p 1424 a toposition p* 1424 b to correspond to the eye movement from position 1414a to position 1414 b. As another example, the HMD may tilt forward dueto sliding down the user's nose. As a result, the HMD may calculate anadjustment for the virtual camera corresponding to the tilt. In thisexample, the HMD may adjust the z value as well as the y value in therendering coordinate system 1422 because the relative position betweenthe eye 1410 and the HMD 1420 may change along both the w-axis and thev-axis. As yet another example, the HMD may tilt to one side (such as tothe right or to the left) so that the position of the eye with respectto the HMD may change along the v-axis and the u-axis. Accordingly, thewearable device can adjust the virtual camera along the y-axis and/orthe x-axis.

Because the position of the virtual camera is adjusted, the renderinglocations of the virtual objects in the user's FOV may also be adjustedaccordingly. Additionally or alternatively, the wearable device canshift the rendering coordinate system (e.g. the shift the x-y-zcoordinate shown in FIGS. 14A and B) for adjusting a relative positionchange between the user's face and the HMD. For example, in FIG. 14B,the shift of the rendering coordinate system 1422 may correspond to themovement of the eye 1410 as relative to the HMD. As a result of thecoordinate shift, the virtual object 1430 may still be at the sameposition in the rendering coordinate system 1422. The virtual object1430 may also appear to be at the same location relative to the physicalobjects in the environment. The displacement of the coordinate systemmay be a displacement of a planar coordinate system such as the x-yplane, the y-z plane, or the x-z plane, or may be a displacement in the3D space.

Shifting of the rendering coordinate system may be an approximation tothe shifting of the virtual camera or the virtual objects. In somesituations (particularly for small adjustments), this approximation maybe close enough to the adjustments generated by displacing the virtualcamera. Additionally, the coordinate shift may be advantageous forreducing the cost of calculating the positions of the virtual object andthereby increase the performance of the wearable device. It may also beadvantageous for reducing delay. For example, the coordinate shift maybe done after the rendering pipeline for the uncorrected coordinate iscomplete and the HMD can apply the coordinate shift just before thevirtual images are mapped to the rendering coordinate system.

In some situations, when the change in the relative position exceeds athreshold level, the wearable device may generate an alert indicatingthe relative position change instead of adjusting the renderinglocations of the virtual objects. In other situations, when the changein the relative position is less than a threshold level, the wearabledevice may be configured not to adjust the rendering locations of thevirtual objects because such a small change in the relative position maynot affect the user's visual experience. The dynamic renderingadjustment described herein can be performed periodically (e.g., every0.5 s, 1 s, 5 s, 10 s, 30 s, etc.) or as needed (e.g., when the HMDrelative position shift is greater than a threshold amount).

Example Process of Adjusting a Rendering Location of a Virtual Object

FIG. 16 illustrates an example process for adjusting a renderinglocation of a virtual object. The process 1600 in FIG. 16 may beperformed by the wearable device which may include an HMD that canpresent virtual objects into physical objects from a perspective of theuser's eyes and include an inward-facing imaging system configured toimage the periocular region of the user.

At block 1610, the wearable device can receive images obtained by theinward-facing imaging system. For example, the wearable device canreceive images from both eye cameras of the inward-facing imagingsystem. In some implementations, the inward-facing imaging system mayinclude only one eye camera. The eye camera may be configured to imagethe periocular region of one eye or the periocular regions for botheyes. The images can be still images or frames from a video (e.g.,typically the inward-facing imaging system comprises video cameras).

At block 1620, the wearable device can analyze the images to identifyperiocular features. For example, the wearable device may use only oneimage or a series of images (such as a video to make such analysis). Asdescribed with reference to FIGS. 11, 12A, and 12B, the wearable devicemay represent the periocular features in a series of 3D keypoints or ina dense map. The wearable device can use a machine learning model suchas a deep neural network to identify the relevant periocular features.

At block 1630, the wearable device can determine a relative positionbetween the HMD and the user's head. For example, the wearable devicecan track keypoints associated with periocular features in a series ofimages acquired by the wearable device. The wearable device can alsomatch a region of the perioculus with a region in a dense map todetermine the relative position between the HMD and the user's face. Asanother example, the wearable device may use the distance between theuser and the inward-facing imaging system to determine whether the HMDtilts (to the side or forward). If the distance calculated for the lefteye is different from the distance calculated for the right eye, thewearable device may determine that the HMD is tilted to one side. If thedistances associated with for the left eye and the right eye are roughlythe same but the distance exceeds a threshold distance, the wearabledevice may determine that it tilts forward because it's far away fromuser's eyes.

In addition to or in alternative to distance between the user's eyes andthe HMD, the wearable device can also use other factors, alone or incombination, to determine the relative position between the user and theHMD. These factors may include alignment between the pupils and thecenter of the display, asymmetry of user's eyes, and so on.

At block 1640, the wearable device can adjust a rendering location ofthe a virtual object based at least partly on the relative positionbetween the HMD and the user's face. For example, the wearable devicecan determine a current position of the eye and calculate an adjustmentbased on the relative position between the eye and the HMD. Theadjustment may be relative to a normal resting position of the eye orthe HMD. The adjustment may be in one or more directions, such as ahorizontal shift, a vertical shift, a depth shift, or a tilt to a side.The wearable device can update the location a virtual camera of arendering system to reflect the adjustment, where the virtual camera maycorrespond to the perspective of the user's eye. The wearable device canalso shift the rendering coordinate system of the HMD to reflect theadjustment.

At block 1650, the HMD renders the virtual object at the adjustedrendering location. The virtual object may be perceived to be at thesame location in the user's environment due the adjustment even thoughthe illuminated pixels associated with the virtual object may be shiftedon the HMD.

In some situations, the wearable device can continuously or periodically(e.g., every 0.5, 1 s, 10 s, 1 min, etc.) monitor the position of theHMD relative to the user's head while the user is wearing the HMDbecause the position of the HMD may change as the user moves around (forexample, the HMD may slide down the user's nose). The wearable devicemay change the AR or VR display (such as adjusting pixels or thelocation associated with the virtual object) to compensate for thechange in the relative position between the HMD and the user's headperiodically or continuously or as needed. This implementation may beparticularly advantageous for maintaining a 3D view without requiringthe 3D display to be constantly located at a particular place on theuser's head. Accordingly, the wearable device can dynamically adjust theprojection of light from the AR or VR display (e.g., a light field) tocompensate for where the HMD is positioned on the user's head.

Glasses Fit

The HMD can use a variety of factors to determine how the HMD fits theuser. As one example, the HMD can analyze the images obtained by the eyecameras by applying a mapping learned via machine learning techniques.The images acquired by the eye cameras can be trained using a machinelearning model to identify periocular features. The machine learningmodel can include a mapping of an image space to a fit space for theHMD. The mapping can be applied to an eye image to determine whether theperiocular region is present in the eye image (e.g., for determiningwhether the HMD is on the user's face) or the quality of the fit of theHMD on the user's face. In some implementations, one mapping may be usedfor both classifying the fit of the wearable device and for determiningwhether the periocular region is present or different mappings may beused for fit and for whether the HMD is on the user's face.

The mapping may incorporate a variety of parameters for determining thefit, such as for example, the appearance of the periocular features inthe images (e.g., whether periocular features for the two eyes appearasymmetrical), the distance from one or both eyes to the HMD,interpupillary distance (e.g., comparing the interpupillary distancecalculated based on images with a proper interpupillary distance for theuser), or relative centers of the pupils (e.g., whether the center ofthe HMD's display aligns with the centers of the pupil).

The image space may include images of periocular regions or images offeatures in the periocular region. The fit space for an HMD may includeinterpupillary distance, alignment between the pupils and the center ofthe display, asymmetry of user's eyes, tilt of the HMD, and so on. Themachine learning model can identify features that are predictors of thegoodness of fit so that the mapping can be applied to an eye image todetermine a quality of fit (e.g., good, acceptable, poor, or a grade,e.g., A-F, or a numerical fit score). The mapping for determiningwhether the HMD is on or off the user's face may be a Boolean value(e.g., on or off).

Various machine learning algorithms may be used for this process. Someexamples of machine learning algorithms that can be used to generate andupdate the models can include supervised or non-supervised machinelearning algorithms, including regression algorithms (such as, forexample, Ordinary Least Squares Regression), instance-based algorithms(such as, for example, Learning Vector Quantization), decision treealgorithms (such as, for example, classification and regression trees),Bayesian algorithms (such as, for example, Naive Bayes), clusteringalgorithms (such as, for example, k-means clustering), association rulelearning algorithms (such as, for example, a-priori algorithms),artificial neural network algorithms (such as, for example, Perceptron),deep learning algorithms (such as, for example, Deep Boltzmann Machine),dimensionality reduction algorithms (such as, for example, PrincipalComponent Analysis), ensemble algorithms (such as, for example, StackedGeneralization), and/or other machine learning algorithms.

In some embodiments, individual models can be customized for individualdata sets. For example, the wearable device can generate a base model.The base model may be used as a starting point to generate additionalmodels specific to a data type (e.g., a particular user), a data set(e.g., a set of additional images obtained), conditional situations(e.g., fit during gameplay may be different than fit during Internetbrowsing), or other variations. In some embodiments, the wearable devicecan be configured to utilize a plurality of techniques to generatemodels for analysis of the aggregated data. Other techniques may includeusing pre-defined thresholds or data values. Over time, the wearabledevice can continue to update the models.

The HMD can determine the fit using quantitative and/or qualitativemeasures. For example, the HMD can generate a score indicating the fitbased on the relative position between the HMD and the user, or based onthe pose of the HMD. The score may be an output of the mapping learnedvia machine learning techniques. In some implementations, a high scoremay indicate that the HMD fits the user well while a low score mayindicate that the HMD does not fit very well. In other implementations,a high score may indicate that HMD does not fit the user well while alow score may indicate the HMD fits well. As another example, the HMDcategorize how well it fits the user. The categories may include “fitswell”, “fits poorly, or “not fit at all”. The categories may also beletter grades such as “A”, “B”, “C”, “D”, and so on. The categories mayalso be the output of the mapping learned from the machine learningtechniques. For example, the mapping may include a correlation betweenan appearance of the periocular feature and a category of fit. Thewearable device can output a certain category of fit based on theappearance of the periocular feature as determined from the imagesacquired by the eye cameras.

Example Processes for Determining Fit of a Wearable Device

FIG. 15A illustrates an example method for determining a fit of thewearable device. The process 1500 a may be performed by the wearabledevice such as an HMD described with reference to FIGS. 2 and 4. The HMDmay have an inward-facing imaging system configured to image theperiocular region of the user.

At block 1502, the HMD can receive images obtained by the inward-facingimaging system. For example, the HMD can receive images for both eyecameras of the inward-facing imaging system. In some implementations,the inward-facing imaging system may include only one eye camera. Theeye camera may be configured to image the periocular region of one eyeor the periocular regions for both eyes. The images can be still imagesor frames from a video (e.g., typically the inward-facing imaging systemcomprises video cameras).

At block 1504, the HMD can analyze the images to identify periocularfeatures. For example, the HMD may use only one image or a series ofimages (such as a video to make such analysis). As described withreference to FIGS. 11 and 12A-B, the HMD may represent the periocularfeatures in a series of 3D points. The HMD can use a machine learningmodel such as deep neural network to identify the relevant periocularfeatures.

At block 1506, the HMD can determine a relative position between the HMDand the user's head. For example, the HMD can analyze the image todetermine whether one or more periocular features appear in the image.If the periocular features do not appear in the image, the HMD maydetermine that the user is not currently wearing the HMD. If theperiocular features appear in the image, the HMD can analyze whether theHMD properly fits the user's face. For example, the HMD may use thedistance between the user and the inward-facing imaging system todetermine whether the HMD tilts (to the side or forward). As an example,if the distance calculated for the left eye is different from thedistance calculated for the right eye, the HMD may determine that theHMD is tilted to one side. As another example, if the distancesassociated with for the left eye and the right eye are roughly the samebut the distance exceeds a threshold distance, the HMD may determinethat it tilts forward because it's far away from user's eyes.

In addition to or in alternative to distance between the user's eyes andthe HMD, the HMD can also use other factors, alone or in combination, todetermine the relative position between the user and the HMD. Thesefactors may include interpupillary distance, alignment between thepupils and the center of the display, asymmetry of user's eyes, and soon.

At block 1508, the HMD can determine a fit category based on therelative position. As described herein, a machine learned mapping can beapplied to an eye image to determine goodness of fit. The HMD canclassify the fit into different categories such as “fits well”, “fitspoorly”, and “not fit at all.” The HMD can also indicate the fitcategory through a user interface. For example, the HMD may provide awarning sign when the HMD fits poorly. As another example, the HMD mayprovide an indicator in the user interface if the HMD fits well. In someimplementations, the HMD may provide a score associated with fit. TheHMD can display the score to the user via the user interface. In someembodiments, fit categories may each be associated with a range of thescores. The HMD can accordingly inform the user the fit category basedon whether the score falls within a given range.

In some situations, the HMD can continuously or periodically (e.g.,every 1 s, 10 s, 1 min, etc.) monitor the position of the HMD relativeto the user's head while the user is wearing the HMD because theposition of the HMD may change as the user moves around (for example,the HMD may slide down the user's nose). The HMD may change the AR or VRdisplay (such as adjusting pixels or the location associated with thevirtual object) to compensate for the change in the relative positionbetween the HMD and the user's head. This implementation may beparticularly advantageous for maintaining a 3D view without requiringthe 3D display to be constantly located at a particular place on theuser's head. Accordingly, the HMD can dynamically adjust the projectionof light from the AR/VR/MR display (e.g., a light field) to compensatefor where the HMD is positioned on the user's head.

Glasses Removal

As described with reference to FIGS. 13A, 13B, and 13C, the wearabledevice can analyze the images acquired by the inward-facing imagingsystem and use various factors to determine relative positions betweenthe user and the wearable device, such as whether the wearable devicetilts to the side or forward.

The information on the relative positions can also be used to determinewhether the user is currently wearing the wearable device. For example,the wearable device can identify periocular features in the imageacquired by the inward-facing imaging system. If the wearable devicedoes not identify any periocular features, the wearable device maydetermine that the user is not wearing the wearable device. In othersituations, the wearable device may calculate a likelihood that the userhas removed the wearable device based on a degree of presence of theuser's periocular features. For example, the wearable device maydetermine that periocular features in the images are sufficiently small(e.g., below a threshold size) that the device has been removed from theuser's head. As another example, the wearable device may calculate how apercentage likelihood that the user has removed the wearable device andcompare the percentage likelihood with a threshold value. If thepercentage likelihood is above the threshold value, the wearable systemmay indicate that the wearable device has been removed from the user'shead. On the other hand, the wearable system can calculate that apercentage likelihood that the user is still wearing the wearable deviceand compare that value against a threshold value on the likelihood ofthat the user is wearing the wearable device. If the percentagelikelihood drops below the threshold value, the wearable device maydetermine that the user has removed the wearable device.

As another example, the wearable device can analyze a series of imagesacquired by the inward-facing imaging system. For example, although theperiocular features do not appear in the first several images in theseries, the inward-facing imaging system can discover periocularfeatures in later acquired images. As a result, the wearable device maydetermine that the user just put on the wearable device. On the otherhand, the periocular features may initially appear in the images, butthe wearable device later discovers that the periocular features are nolonger in the present FOV (or are sufficiently small). The wearabledevice can then determine that the user has taken off the wearabledevice.

Additionally or alternatively, the wearable device may use distance,size of the periocular features, and/or other factors to determinewhether the wearable device is in place or has been removed. Forexample, although the wearable device may detect periocular features inan image, the periocular features may appear to be too small. As aresult, the wearable device may determine that the distance between thewearable device and the user may be sufficiently far such that the useris not currently wearing the wearable device.

The wearable device can use other sensors together with theinward-facing imaging system to determine the relative position betweenthe user and the wearable device. For example, the wearable device mayuse the sensors described herein, such as IMUs (e.g., accelerometers orgyroscopes), and so on, to detect a movement of the wearable device.This information of movement may be used together with image analysis todetermine whether a user has taken off or put on the wearable device. Asan example, the wearable device may detect an acceleration of thewearable device while acquiring a series of images using theinward-facing imaging system. If the wearable device does not detect theperiocular region in an initial image of the series of images, thewearable device may determine that the user is putting on the device. Onthe other hand, if the periocular region was in the images acquired bythe inward-facing imaging system and the wearable device detects anacceleration of the wearable device, the wearable device may determinethat the user has removed the wearable device.

As another example, the wearable device may have a pressure sensor. Thepressure sensor may be located at the temple (such as the earpieces) ofglasses, or the nose pad of the wearable device. When the wearabledevice is put onto the user's face, the pressure sensor may send asignal indicating that the wearable device touches the user. On theother hand, when the user takes off the wearable device, the pressuresensor may acquire data suggesting that it no longer presses the user.This information may be combined with the images acquired by theinward-facing imaging system to determine whether the user has taken offor put on the wearable device.

Once the wearable device determined that it has been removed from theuser's head, the wearable device may accordingly send a signal whichturns off one or more functions of the wearable device or enter powersaving mode when the wearable device is removed from the user. On theother hand, when the wearable device determines that the user has put onthe device, the wearable device may send a signal which turns on afunction (such as the activation of the AR/VR/MR display) of thewearable device.

The wearable device can also adjust the 3D display based on the relativeposition of the wearable device and the user's eyes. For example, whenthe wearable device detects that the device slips down the user's nose,the wearable device may shift the location of the pixels or change theposition of a virtual object in the 3D user interface to accommodatethis change in position. This implementation may provide a realistic andstable 3D display while the user is moving around in his environment.

The wearable device can continuously monitor whether the periocularregion appears in the images. The wearable device can also select animage among a series of images acquired by the inward-facing imagingsystem, and determine whether the periocular region appears in thatimage. The continuous monitoring can occur at closely spaced timeintervals, which may be periodic (e.g., every second, every 10 seconds,every minute, every 15 minutes, etc.).

In some embodiments, the inward-facing imaging system may continuouslyobtain images in its FOV. The inward-facing imaging system, however, mayalso start or stop imaging in response to a trigger. For example, thewearable device may be configured to start imaging the user's face whenit detects that the user is putting on the wearable device. The wearabledevice can use various sensors described with reference to FIGS. 2 and7, such as an accelerometer and/or a gyroscope, for the detection. Thedata acquired by the sensors may be analyzed against a threshold level.If the data passes the threshold level, the wearable device may start orstop the imaging process. As an example, when a user lifts up thewearable device, the accelerometer of the wearable device may acquiredata on the acceleration of the wearable device. If the wearable devicedetermines that the acceleration exceeds certain threshold acceleration,the wearable device may begin to image the user's face. Once the userputs the wearable device, for example, on his head, the acceleration maydecrease. If the wearable device determines that the acceleration hasreduced to a certain threshold, the wearable device may stop takingimages of the user's face.

Another trigger may be the distance between the wearable device and theuser. For example, the sensors may emit and receive signals such asacoustic or optical signals, and use the signals or the feedback of thesignals to measure the distance. The wearable device may also determinethe distance by analyzing images acquired by the inward-facing imagingsystem. For example, the wearable device may calculate the distancebased on the size of the face in the image, where a big size mayindicate a small distance while a small size may indicate a largedistance. The wearable device may image the user when the distancepasses a threshold or is within a certain range. For example, thewearable device may only image the user when the wearable device iswithin a certain distance to the user.

Example Processes for Determining Removal of a Wearable Device

FIG. 15B illustrates an example method for determining a removal of thewearable device. The process 1500 b in FIG. 15B may be performed by thewearable device such as an HMD described with reference to FIGS. 2 and4. The HMD may have an inward-facing imaging system configured to imagethe periocular region of the user.

At block 1510, the inward-facing imaging system can acquire a series ofimages. The HMD can receive the images acquired by the inward-facingimaging system. The series of images may be taken in a sequence. Forexample, the series of images may include frames of images at differenttimestamps of a video.

At block 1520, the HMD can analyze one or more images acquired in block1510 to identify periocular features. As described with reference toFIGS. 11 and 12A-B, the periocular features may be mathematicalrepresentations (such as points in the 3D space) of the facial features.The HMD can use machine learning techniques, such as deep neuralnetwork, to identify the periocular features.

In some implementations, the HMD may reduce the resolution of theincoming images or ignore a portion of the image (such as center portionof the perioculus, including the iris and the sclera) and therebyincrease the image processing speed. These implementations may beadvantageous because the center portion of the perioculus may havedetailed characteristics which may not significantly affect thedetermination of whether an HMD is on the user. Furthermore, the scleramay create specular reflections of objects in the environment. Thesespecular reflections and detailed characteristics of perioculus canintroduce noise to the machine learning models and decrease the accuracyof the analysis.

At block 1530, the HMD can determine whether the periocular features donot appear in the acquired images. If the HMD determines that one ormore periocular features do not appear in the acquired images, the HMDcan emit a signal indicating that the HMD has been removed from theuser's head at block 1532. The signal may cause the HMD to power off orenter sleep mode to reduce battery power consumption. The signal mayalso cause the HMD to stop performing certain functions. For example,the signal may cause the HMD to turn off the VR or AR mode. The emittedsignal could be an electronic signal but may additionally oralternatively include an audible or visible signal as a warning to theuser.

If the HMD determines that the periocular region only appears in asubset of the image at block 1540, the HMD may indicate a change ofstate for the HMD at block 1542. For example, the HMD may determine thatthe periocular features appear in an initial image but not a laterimage. Based on this determination, the HMD may indicate that the userhas taken off the HMD. In some implementations, the HMD may send asignal indicating that the user has taken off the HMD which may causethe same actions as shown in block 1532 to be performed.

On the other hand, the HMD may detect that the periocular features onlyappear in a later image. Accordingly, the HMD may determine that theuser has put on the device. In response to this determination, the HMDmay turn on the virtual reality or augmented reality function of theHMD, initiate a user login sequence, change the resolution of theinward-facing imaging system (e.g., to a resolution more suitable foreye-tracking or iris recognition), or perform other actions to reflectthis change of state.

However, if the HMD detects periocular features in both the initiallyacquired images and later acquired images, the HMD may determine thatthe user is currently wearing the HMD at block 1544. Accordingly, theHMD may optionally perform the block 1510.

Although the examples are described with reference to detecting theperiocular region, these techniques described herein are not limited tothe periocular region. For example, the techniques described herein canalso be used to detect other facial features or portions of the user'sbody. In addition, the blocks shown in FIG. 15B are not required to beperformed in a sequence because some blocks may be performed before,after, or at the same time as another block. For example, the decisionblock 1540 is not required to be performed after the decision block1530. Furthermore, the method 1500 b is not required to include allblocks shown in FIG. 15B, and the method 1500 may include more or fewerblocks. For example, one or more blocks (such as blocks 1530 and 1540)in FIG. 15B may be combined in single block.

Example Processes of Generating a Mapping from a Periocular Image to aRelative Position Between a Wearable Device and the User's Face

FIG. 15C is an example of a method 1500 c for applying a machinelearning technique to provide a mapping for goodness of fit or whetherthe HMD is on the user. At block 1552, eye images are accessed, whichmay be used as training images in a supervised or unsupervised learningto generate the mapping. At block 1554, the machine learning technique(e.g., a deep neural network) is used to analyze the eye images todevelop the mapping that can be used for quality of fit of the HMD onthe user or an HMD removal categorization (e.g., on or off the user'shead). At block 1556, the mapping is the output (e.g., output from atrained neural network) and can be stored in a memory associated with aparticular HMD (e.g., the local data module 260 or the remote datarepository 280). At block 1562, additional eye images can be accessed,which may be particular to a user. For example, the user can stand infront of a mirror and move the HMD around on the user's face and notatethe user's subjective impression of the quality of the resulting fit. Atblock 1564, the machine learning technique updates the mapping toreflect the user's individual preferences, e.g., by further training aneural network.

At block 1566, the HMD can provide to the user information about thequality of the measured fit of the HMD on the user's head. For example,the HMD may display a quality grade to the user or emit an audiblesignal indicating quality of fit. The HMD may display instructions tothe user (or provide audible instructions) on how to improve the fit ofthe HMD on the user's head, acquire additional eye images (block 1562),and determine an updated quality of fit (block 1564). The HMD may repeatthis until the quality of fit of the HMD on the user's head is at anacceptable or optimal level. Accordingly, the HMD may lead the userthrough a quality fitting procedure to ensure the fit of the HMD on theparticular user's head is suitable. The HMD may perform this qualityfitting procedure the first time a particular user puts on the HMD,periodically, or when the measured quality of fit is below a threshold(e.g., on an A-F grade scale, when the quality grade is below a C).

Blocks 1562, 1564, 1566 are optional but provide an advantageous levelof user customization. Further, the mapping can be customized for eachof a number of users of the HMD (e.g., a family may share use of asingle HMD and can customize a mapping for each family member).

Additional Aspects

In a 1st aspect, a method for detecting removal of a head-mounteddisplay from the head of a user, the method comprising: under control ofthe head-mounted display comprising computer processor and aninward-facing imaging system configured to image a periocular region ofa user: receiving an image from the inward-facing imaging system;analyzing the image to identify a degree of presence of periocularfeatures of the periocular region in the image; determining, based atleast in part on the degree of presence of the periocular features, alikelihood that the head-mounted display has been removed from the headof the user; and in response to a determination that the likelihoodpasses a threshold, providing an indication that the head-mounteddisplay has been removed from the head of the user.

In a 2nd aspect, the method of aspect 1, wherein the inward-facingimaging system comprises two eye cameras, wherein each eye camera isconfigured to image a respective eye of the user.

In a 3rd aspect, the method of any one of aspects 1-2, wherein theperiocular region comprises one or more of the following: eye sockets, aportion of a nose, a portion of cheeks, a portion of eyebrows, or aportion of a forehead of the user.

In a 4th aspect, the method of any one of aspects 1-3, wherein analyzingthe image is performed by a sparse auto-encoder algorithm, a clusteringalgorithm, a deep neural network, or any type of neural network.

In a 5th aspect, the method of any one of aspects 1-4, whereindetermining, based at least in part on the degree of presence ofperiocular features, the likelihood that the head-mounted display hasbeen removed from the head of the user, comprises determining a presenceof periocular features specific to the user of the head-mounted display.

In a 6th aspect, the method of any one of aspects 1-5, wherein analyzingthe image comprises reducing a resolution of the image.

In a 7th aspect, the method of any one of aspects 1-6, wherein analyzingthe image comprises masking out a portion of the periocular region, theportion comprising at least one of the following: an iris, a sclera ofan eye, or a specular reflection on an eyeball.

In an 8th aspect, the method of any one of aspects 1-7, whereinproviding the indication comprises providing a signal that causes thehead-mounted display to power off or to enter a battery saving mode.

In a 9th aspect, a system for detecting removal of a head-mounteddisplay from the head of a user, the system comprising an inward-facingimaging system configured to image a periocular region of a user and acomputer processor configured to perform any one of the methods inaspects 1-8.

In a 10th aspect, a method for determining a location of a head-mounteddisplay with respect to the head of a user, the method comprising: undercontrol of the head-mounted display comprising computer processor and aninward-facing imaging system configured to image a periocular region ofa user: receiving a first image and a second image from theinward-facing imaging system, wherein the first image and the secondimage are acquired in a sequence; analyzing the first image and thesecond image to identify periocular features of the periocular region inthe first image and the second image; determining whether the periocularregion is in the first image or the second image based at least partlyon the periocular features; in response to a determination that theperiocular region is in neither the first image nor the second image,providing an indication that the head-mounted display has been removedfrom the head of the user; and in response to a determination that theperiocular region is in either the first image or the second image,indicating a change of a state for the head-mounted display.

In an 11th aspect, the method of aspect 10, wherein the first image isacquired before the second image.

In a 12th aspect, the method of any one of aspects 10-11, wherein theinward-facing imaging system comprises two eye cameras, each isconfigured to image an eye of the user.

In a 13th aspect, the method of any one of aspects 10-12, wherein theperiocular region comprises one or more of the following: an eye socket,a portion of a nose, a portion of a cheek, a portion of an eyebrow, or aportion of a forehead of the user.

In a 14th aspect, the method of any one of aspects 10-13, whereinidentifying the periocular features is performed by a deep neuralnetwork, a sparse auto-encoder algorithm, a clustering algorithm, or anytype of neural network.

In a 15th aspect, the method of any one of aspects 10-14, wherein theperiocular features are specific to the user of the head-mounteddisplay.

In a 16th aspect, the method of any one of aspects 10-15, whereinanalyzing the first image and the second image comprises reducing aresolution of the first image and the second image.

In a 17th aspect, the method of any one of aspects 10-16, whereinanalyzing the first image and the second image comprises masking out, inthe first image and the second image, a portion of the periocularregion, the portion comprises at least one of the following: an iris, asclera of an eye, or a specular reflection on an eyeball.

In a 18th aspect, the method of any one of aspects 10-17, whereinindicating the change of the state for the head-mounted displaycomprises powering off or entering a battery saving mode.

In a 19th aspect, the method of any one of aspects 10-18, wherein thestate of the head-mounted display comprises that head-mounted display ison the head of the user or that head-mounted display is removed from thehead of the user.

In a 20th aspect, the method of aspect 19, wherein: in response todetermining that the periocular features are identified in the firstimage but not the second image, changing the state of the head-mounteddisplay from being on the head of the user to be removed from the headof the user, and in response to determining that the periocular featuresare identified in the second image but not in the first image, changingthe state of the head-mounted display from being removed from the headof the user to be on the head of the user.

In a 21st aspect, a system for determining a location of a head-mounteddisplay with respect to the head of a user, the system comprising aninward-facing imaging system configured to image a periocular region ofa user and a computer processor configured to perform any one of themethods in aspects 10-20.

In a 22nd aspect, a method for determining a fit of a head-mounteddisplay on the head of a user, the method comprising: under control ofthe head-mounted display comprising computer processor and aninward-facing imaging system configured to image a periocular region ofa user: receiving an image from the inward-facing imaging system;analyzing the image to identify periocular features of the periocularregion; determining a relative position between the head-mounted displayand the head of the user based at least partly on an analysis of theimage; and determining a fit category of the head-mounted display basedat least partly on the relative position between the head-mounteddisplay and the head of the user.

In a 23rd aspect, the method of aspect 22, wherein the inward-facingimaging system comprises two eye cameras, with each eye cameraconfigured to image a respective eye of the user.

In a 24th aspect, the method of any one of aspects 22-23, wherein theperiocular region comprises one or more of the following: eye sockets, aportion of a nose, a portion of cheeks, a portion of eyebrows, or aportion of a forehead of the user.

In a 25th aspect, the method of any one of aspects 22-24, wherein theperiocular features are specific to the user of the head-mounteddisplay.

In a 26th aspect, the method of any one of aspects 22-25, wherein theperiocular features are identified using at least one of: a clusteringalgorithm or a neural network algorithm.

In a 27th aspect, the method of any one of aspects 22-26, wherein thedetermining the relative position comprises one or more of thefollowing: calculating a distance with the head-mounted display to eyesof the user; calculating an interpupillary distance between the eyes ofthe user; determining a relative position between the head-mounteddisplay and the eyes of the user; determining an asymmetry of the eyesof the user; or determining a tilt of the head-mounted display, whereinthe tilt comprises at least one of a forward tilt or a side tilt.

In a 28th aspect, the method of any one of aspects 22-27, whereindetermining the fit category comprises: comparing the relative positionbetween the HMD and the head of the user with a threshold condition; andassociating the fit category with the HMD based at least partly on thecomparison between the relative position and the threshold condition.

In a 29th aspect, the method of any one of aspects 22-28, wherein thefit category comprises at least one of: fit well, fit adequately, or fitpoorly.

In a 30th aspect, the method of any one of aspects 22-29, furthercomprising: providing an indication of the fit category, wherein theindication causes the head-mounted display to perform at least one ofthe following: adjusting a display of the head-mounted display tocompensate for the relative position, or providing an alert to the userassociated with the fit category.

In a 31st aspect, a system for determining a fit of a head-mounteddisplay on a user, the system comprising: a hardware computer processorprogrammed to: access an image of a periocular region of a user; accessa mapping between images of the periocular region and a goodness of fitof the head-mounted display on the user; and apply the mapping todetermine the goodness of fit of the head-mounted display.

In a 32nd aspect, the system of aspect 31, wherein the mapping isgenerated by a neural network.

In a 33rd aspect, the system of any one of aspects 31-32, wherein thegoodness of fit comprises at least one of a qualitative rating, anumerical rating, or a letter grade.

In a 34th aspect, the system of any one of aspects 31-33, wherein thecomputer processor is further programmed to perform a corrective actionin response to the goodness of fit passing a threshold level.

In a 35th aspect, the system of aspect 34, wherein to perform thecorrective action, the computer processor is programmed to: provide anindication to the user, or adjust a display of the head-mounted displayto compensate for the fit.

In a 36th aspect, the system of aspect 35, wherein to provide theindication to the user, the computer processor is programmed to provideinstructions to the user on improving the goodness of fit.

In a 37th aspect, the system of any one of aspects 34-35, wherein toprovide the indication to the user, the computer processor is programmedto: access an additional eye image; apply the mapping to the additionaleye image to determine an updated goodness of fit.

In a 38th aspect, the system of aspect 37, wherein the computerprocessor is programmed to continue to access additional eye images anddetermine updated goodnesses of fit until an updated goodness of fitpasses a threshold.

In a 39th aspect, an augmented reality (AR) system for adjusting arendering location of a virtual object, the AR system comprising: an ARdisplay system configured to render a virtual object onto athree-dimensional space (3D); an imaging system configured to image aperiocular portion of a user's face; and a hardware processor programmedto: receive an image from the imaging system; analyze the image toidentify periocular features of the user; determine a relative positionbetween the AR display system and the head of the user based at leastpartly on the periocular features; adjust a rendering location of thevirtual object based at least partly on the relative position betweenthe AR display system and the head of the user; and instruct theaugmented reality display system to render the virtual object at therendering location.

In a 40th aspect, the AR system of aspect 39, wherein to adjust therendering location, the hardware processor is programmed to: determine anormal resting position of the user's eye and a normal renderingposition of the virtual object which corresponds to the normal restingposition of the user's eye; calculate a correction to the normalrendering position of the virtual object based at least partly on therelative position between the AR display system and the head of theuser; and determine the rendering location of the virtual object basedon the correction to the normal rendering position.

In a 41st aspect, the AR system of aspect 40, wherein the normalrendering position of the user's eye and the normal rendering positionof the virtual object are determined during an initiation phase of theAR system.

In a 42nd aspect, the AR system of any one of aspects 39-41, wherein thehardware processor is further programmed to: analyze the image todetermine an eye pose; and wherein a position of the user's eye iscorresponding to a rendering viewpoint in the AR display system, andwherein to adjust the rendering location comprises to update a positionof the rendering viewpoint based on the eye pose of the user.

In a 43rd aspect, the AR system of aspect 42, wherein to update theposition of the rendering viewpoint comprises shifting a coordinateassociated with the AR display system.

In a 44th aspect, the AR system of any one of aspects 39-43, wherein theAR display system comprises a spatial AR display system configured torender a virtual object from a perspective of the user's eye.

In a 45th aspect, the AR system of any one of aspects 39-44, wherein theimaging system comprises an outward-facing imaging system configured toimage an environment of the user and wherein the image from the imagingsystem comprises a reflected image of the user's face.

In a 46th aspect, the AR system of any one of aspects 39-45, wherein therelative position between the AR display system and the head of the usercomprises one or more of: a horizontal shift, a vertical shift, a depthshift, a tilt to a side, or a forward tilt.

In a 47th aspect, the AR system of any one of aspects 39-46, wherein therelative position between the AR display system and the head of the usercomprises a first relative position for a first eye of the user and asecond relative position for a second eye of the user.

In a 48th aspect, the AR system of any one of aspects 39-47, wherein therelative position is determined by at least one of: tracking theperiocular features using visual keypoints or matching a region of theface with a dense map encoding at least a portion of the head of theuser.

In a 49th aspect, the AR system of aspect 48, wherein the visualkeypoints are computed using at least one of: scale-invariant featuretransform, speeded up robust features, oriented FAST and rotated BRIEF,binary robust invariant scalable keypoints, or fast retina keypoint; orwherein the dense map is calculated using iterative closest pointalgorithm.

In a 50th aspect, a method for adjusting a rendering location of avirtual object in an augmented reality device (ARD), the methodcomprising: under control of an ARD comprising a hardware processor, anaugmented reality display system configured to render a virtual objectonto a three-dimensional space (3D), and an imaging system configured toimage a periocular portion of a user's face: receiving an image from theimaging system; analyzing the image to identify periocular features ofthe user; determining a relative position between the ARD and the headof the user based at least partly on the periocular features; adjustinga rendering location of the virtual object based at least partly on therelative position between the ARD and the head of the user of the user;and rendering, by the augmented reality display system, the virtualobject at the rendering location.

In a 51st aspect, the method of aspect 50, wherein adjusting therendering location comprises: determining a normal resting position ofthe user's eye and a normal rendering position of the virtual objectwhich corresponds to the normal resting position of the user's eye;calculating a correction to the normal rendering position of the virtualobject based at least partly on the relative position between the ARDand the head of the user; and determining the rendering location of thevirtual object based on the correction to the normal rendering position.

In a 52nd aspect, the method of aspect 51, wherein the normal renderingposition of the user's eye and the normal rendering position of thevirtual object are determined during an initiation phase of the ARD.

In a 53rd aspect, the method of any one of aspects 50-52, the methodfurther comprising: analyzing the image to determine an eye pose; andwherein a position of the user's eye is corresponding to a renderingviewpoint in the augmented reality display system, and wherein adjustingthe rendering location comprises updating a position of the renderingviewpoint based on the eye pose of the user.

In a 54rd aspect, the method of aspect 53, wherein updating the positionof the rendering viewpoint comprises shifting a coordinate associatedwith the augmented reality display system.

In a 55th aspect, the method of any one of aspects 52-54, wherein theaugmented reality display system comprises a spatial augmented realitydisplay configured to render a virtual object from a perspective of theuser's eye.

In a 56th aspect, the method of any one of aspects 50-55, wherein theimaging system comprises an outward-facing imaging system configured toimage an environment of the user and wherein the image from the imagingsystem comprises a reflected image of the user's face.

In a 57th aspect, the method of any one of aspects 50-56, wherein therelative position between the ARD and the head of the user comprises oneor more of: a horizontal shift, a vertical shift, a depth shift, a tiltto a side, or a forward tilt.

In a 58th aspect, the method of any one of aspects 50-57, wherein therelative position between the ARD and the head of the user comprises afirst relative position for a first eye of the user and a secondrelative position for a second eye of the user.

In a 59th aspect, the method of any one of aspects 50-58, wherein therelative position is determined by at least one of: tracking theperiocular features using visual keypoints or matching a region of theface with a dense map encoding at least a portion of the head of theuser.

In a 60th aspect, the method of aspect 59, wherein the visual keypointsare computed using at least one of: scale-invariant feature transform,speeded up robust features, oriented FAST and rotated BRIEF, binaryrobust invariant scalable keypoints, or fast retina keypoint; or whereinthe dense map is calculated using iterative closest point algorithm.

In a 61st aspect, an augmented reality device (ARD) for adjusting arendering location of a virtual object, the ARD comprising: an augmentedreality display system configured to render a virtual object onto athree-dimensional space (3D); an imaging system configured to acquire animage of a user; a hardware processor programmed to: receive an imagefrom the imaging system; analyze the image to identify features of auser; compute a pose for the ARD relative to the head of the user basedat least partly on the identified features; and instruct the augmentedreality display system to render the virtual object at a renderinglocation that is based at least partly on the pose for the ARD.

In a 62nd aspect, the ARD of aspect 61, wherein the image comprises aperiocular image and the features comprise periocular features.

In a 63rd aspect, the ARD of any one of aspects 61-62, wherein thefeatures are encoded by at least one of: visual keypoints or a dense mapassociated with a face model of the user.

In a 64th aspect, the ARD of aspect 63, wherein to compute the pose forthe ARD, the hardware processor is programmed to track the visualkeypoints or matching a region of the user's face with the face model.

In a 65th aspect, the ARD of any one of aspects 63-65, wherein thevisual keypoints are computed using at least one of: scale-invariantfeature transform, speeded up robust features, oriented FAST and rotatedBRIEF, binary robust invariant scalable keypoints, or fast retinakeypoint or wherein the dense map is calculated using iterative closestpoint algorithm.

In a 66th aspect, the ARD of any one of aspects 61-65, wherein the posefor the ARD comprises one or more of: a horizontal shift, a verticalshift, a depth shift, a tilt to a side, or a forward tilt.

In a 67th aspect, the ARD of any one of aspects 61-66, wherein toinstruct the augmented reality display system to render the virtualobject at the rendering location that is based at least partly on thepose for the ARD, the hardware processor is programmed to perform atleast one of: displacing a virtual camera in the augmented realitydisplay system, or shifting a rendering coordinate system associatedwith the augmented reality display system to correspond to the pose ofthe ARD.

In a 68th aspect, the ARD of any one of aspects 61-67, wherein theaugmented reality display system comprises a spatial augmented realitydisplay configured to render a virtual object from a perspective of theuser's eye.

In a 69th aspect, a method for adjusting a rendering location of avirtual object in an augmented reality device (ARD), the methodcomprising: under control of an ARD comprising a hardware processor, anaugmented reality display system configured to render a virtual objectonto a three-dimensional space (3D), and an imaging system: receiving animage from the imaging system; analyzing the image to identify featuresof a user; computing a pose for the ARD relative to the head of the userbased at least partly on the identified features; and rendering, by theaugmented reality display system, the virtual object at a renderinglocation that is based at least partly on the pose for the ARD.

In a 70th aspect, the method of aspect 69, wherein the image comprises aperiocular image and the features comprise periocular features.

In a 71st aspect, the method of any one of aspects 69-30, wherein thefeatures are encoded by at least one of: visual keypoints or a dense mapassociated with a face model of the user.

In a 72nd aspect, the method of aspect 31, wherein computing the posefor the ARD comprises tracking the visual keypoints or matching a regionof the user's face with the face model.

In a 73rd aspect, the method of any one of aspects 31-32, wherein thevisual keypoints are computed using at least one of: scale-invariantfeature transform, speeded up robust features, oriented FAST and rotatedBRIEF, binary robust invariant scalable keypoints, or fast retinakeypoint or wherein the dense map is calculated using iterative closestpoint algorithm.

In a 74th aspect, the method of any one of aspects 69-73, wherein thepose for the ARD comprises one or more of: a horizontal shift, avertical shift, a depth shift, a tilt to a side, or a forward tilt.

In a 75th aspect, the method of any one of aspects 69-74, whereinrendering, by the augmented reality display system, the virtual objectat a rendering location that is based at least partly on the pose forthe ARD, comprises at least one of: displacing a virtual camera in theaugmented reality display system or shifting a rendering coordinatesystem associated with the augmented reality display system tocorrespond to the pose of the ARD.

In a 76th aspect, the method of any one of aspects 69-75, wherein theaugmented reality display system comprises a spatial augmented realitydisplay configured to render a virtual object from a perspective of theuser's eye.

In 77th aspect, a head-mounted device (HMD) comprising: a spatialaugmented reality (AR) display system configured to render a virtualobject from a perspective of the user's eye; an inward-facing imagingsystem configured to image a periocular region of a user's face; whereinthe inward-facing imaging system is configured to acquire at least afirst image of the periocular region and is further configured to mask aportion of the periocular region captured in the first image; and ahardware processor programmed to: receive an image from theinward-facing imaging system; analyze the image to identify periocularfeatures of the user by an object recognizer; determine a normal restingposition of the user's eye and a normal rendering position of thevirtual object which corresponds to the normal resting position of theuser's eye; determine a relative position between the HMD and the headof the user based at least partly on the periocular features; calculatean adjustment to the normal rendering position of the virtual objectbased at least partly on the relative position between the HMD and thehead of the user; determine the rendering location of the virtual objectbased on the adjustment to the normal rendering position; and instructthe HMD to render the virtual object at the rendering location. Incertain implementations, the inward-facing imaging system can also beconfigured to acquire the first image at a reduced resolution.

In a 78th aspect, the HMD of aspect 77, wherein the processor isconfigured to determine the normal resting position of the user's eyeand the normal rendering position of the virtual object during aninitialization phase of the HMD.

In a 79th aspect, the HMD of any one of aspects 77-78, wherein todetermine relative position between the HMD and the head of the user,the processor is configured to perform at least one of: tracking theperiocular features using visual keypoints, or matching a region of theface with a dense map encoding at least a portion of the head of theuser.

In an 80th aspect, the HMD of aspect 79, wherein the visual keypointsare computed using at least one of: scale-invariant feature transform,speeded up robust features, oriented FAST and rotated BRIEF, binaryrobust invariant scalable keypoints, or fast retina keypoint; or whereinthe dense map is calculated using iterative closest point algorithm.

In an 81st aspect, the HMD of any one of aspects 77-80, wherein thenormal resting position of the user's eye corresponds to a renderingviewpoint of the HMD, and to adjust the normal rendering position, thehardware processor is programmed to shift a coordinate associated withthe HMD to update the position of the rendering viewpoint.

In an 82nd aspect, the HMD of any one of aspects 77-81, wherein therelative position between the HMD and the head of the user comprises afirst relative position for a first eye of the user and a secondrelative position for a second eye of the user.

In an 83rd aspect, the HMD of any one of aspects 77-82, wherein withrespect to the normal resting position, the relative position betweenthe HMD and the head of the user comprises one or more of: a horizontalshift, a vertical shift, a depth shift, a tilt to a side, or a forwardtilt.

In an 84th aspect, a method comprising: under control of a head-mounteddevice (HMD) comprising a hardware processor, a computer processor, adisplay system configured to render a virtual object, and aninward-facing imaging system configured to image a periocular portion ofa user's face: receiving an image from the inward-facing imaging system;analyzing the image to identify periocular features of the user by anobject recognizer; determining a relative position between the HMD andthe head of the user based at least partly on the periocular features;determining a degree of fit by comparing the relative position with athreshold condition; and causing the HMD to perform at least one of thefollowing: adjusting a display of the HMD to compensate for the relativeposition, and providing an indication to the user associated with thedegree of fit.

In an 85th aspect, the method of aspect 84, wherein the objectrecognizer analyzes the image using at least one of: a sparseauto-encoder algorithm, a clustering algorithm, a deep neural network,and any type of neural network.

In an 86th aspect, the method of any one of aspects 84-85, whereindetermining the relative position comprises at least one of: calculatinga distance with the HMD to eyes of the user; calculating aninterpupillary distance between the eyes of the user; determining arelative position between the HMD and the eyes of the user; determiningan asymmetry of the eyes of the user; or determining a tilt of the HMD,wherein the tilt comprises at least one of a forward tilt or a sidetilt.

In an 87th aspect, the method of any one of aspects 84-86, the methodfurther comprising: adjusting a rendering location of the virtual objectbased at least partly on the relative position between the HMD and thehead of the user; and rendering, by the AR display system, the virtualobject at the rendering location.

In an 88th aspect, the method of aspect 87, wherein adjusting therendering location of the virtual object comprises: determining a normalresting position of the user's eye and a normal rendering position ofthe virtual object which corresponds to the normal resting position ofthe user's eye; calculating an adjustment to the normal renderingposition of the virtual object based at least partly on the relativeposition between the HMD and the head of the user; and determining therendering location of the virtual object based on the adjustment to thenormal rendering position.

In an 89th aspect, the method of any one of aspects 87-88, the methodfurther comprising: analyzing the image to determine an eye pose; andwherein a position of the user's eye is corresponding to a renderingviewpoint in the display system, and wherein adjusting the renderinglocation comprises updating a position of the rendering viewpoint basedon the eye pose of the user.

In a 90th aspect, the method of aspect 89, wherein updating the positionof the rendering viewpoint comprises shifting a coordinate associatedwith the display system.

In a 91st aspect, the method of any one of aspects 84-90, whereindetermining the degree of fit comprises: accessing the image of theperiocular region of the user; accessing a mapping between images of theperiocular region and the degree of fit of the HMD on the user; andapplying the mapping to determine the degree of fit of the HMD.

In a 92nd aspect, the method of any one of aspects 84-91, wherein thedegree of fit comprises at least one of: fit well, fit adequately, orfit poorly.

In a 93rd aspect, the method of any one of aspects 84-92, wherein theindication to the user associated with the degree of fit comprises:providing instructions to the user on improving the degree of fit;accessing an additional eye image; applying the mapping to theadditional eye image to determine an updated degree of fit; andaccessing additional eye images and determine updated degrees of fituntil an updated degree of fit passes a threshold.

In a 94th aspect, the method of any one of aspects 84-93, whereindetermining the relative position comprises: receiving a first image anda second image from the inward-facing imaging system, wherein the firstimage and the second image are acquired in sequence; analyzing the firstimage and the second image to identify periocular features specific tothe user of the periocular region in the first image and the secondimage; determining whether the periocular region is in the first imageor the second image based at least partly on the periocular features;determining the relative position based at least partly on theappearance of the periocular features in the first and the second image;and wherein the method further comprises: in response to a determinationthat the periocular region is in neither the first image nor the secondimage, providing an indication that the HMD has been removed from thehead of the user; and in response to a determination that the periocularfeatures is either the first image or the second image, indicating achange of a state for the HMD.

In a 95th aspect, the method of aspect 94, wherein indicating the changeof the state causes the HMD to power off or enter a battery saving mode.

In a 96th aspect, the method of any one of aspects 94-95, wherein: inresponse to determining that the periocular features are identified inthe first image but not the second image, changing the state of the HMDfrom being on the head of the user to be removed from the head of theuser, and in response to determining that the periocular features areidentified in the second image but not in the first image, changing thestate of the HMD from being removed from the head of the user to be onthe head of the user.

In a 97th aspect, an HMD comprising: a spatial augmented reality (AR)display system configured to render a virtual object from a perspectiveof the user's eye; non-transitory memory configured to store ameasurement of a relative position between the HMD and the head of theuser; and a hardware processor in communication with the non-transitorymemory and the spatial AR display system, the hardware processorprogrammed to: access the measurement of the relative position betweenthe HMD and the head of the user; calculate an adjustment to a normalrendering position of the virtual object based at least partly on therelative position between the HMD and the head of the user; determinethe rendering location of the virtual object based on the adjustment tothe normal rendering position; and instruct the spatial AR displaysystem to render the virtual object at the rendering location.

In a 98th aspect, the HMD of aspect 97, wherein the measurement of therelative position between the HMD and the head of the user is calculatedby performing at least one of: tracking the periocular features usingvisual keypoints, or matching a region of the face with a dense mapencoding at least a portion of the head of the user.

In a 99th aspect, the HMD of aspect 98, wherein the visual keypoints arecomputed using at least one of: scale-invariant feature transform,speeded up robust features, oriented FAST and rotated BRIEF, binaryrobust invariant scalable keypoints, or fast retina keypoint; or whereinthe dense map is calculated using iterative closest point algorithm.

In a 100th aspect, the HMD of any one of aspects 97-99, wherein thehardware processor is further programmed to determine a normal restingposition of the user's eye associated with a rendering viewpoint of theHMD, wherein the normal rendering position of the virtual object whichcorresponds to the normal resting position of the user's eye.

In a 101st aspect, the HMD of aspect 100, wherein to calculate theadjustment to the normal rendering position of the virtual object, thehardware processor is programmed to: determine a shift with respect tothe normal resting position based at least partly on the relativeposition between the HMD and the head of the user; and shift acoordinate associated with the rendering viewpoint of the HMD based atleast partly on the shift with respect to the normal resting position.

In a 102nd aspect, the HMD of aspect 101, wherein the relative positionbetween the HMD and the head of the user comprises one or more of: ahorizontal shift, a vertical shift, a depth shift, a tilt to a side, ora forward tilt with respect to the normal resting position.

In a 103rd aspect, the HMD of any one of aspects 97-102, wherein therelative position between the HMD and the head of the user comprises afirst relative position between the HMD for a first eye of the user anda second relative position for a second eye of the user.

In a 104th aspect, a method comprising: under control of an HMDcomprising a hardware processor, a computer processor, a display systemconfigured to render a virtual object, and an inward-facing imagingsystem configured to image a periocular portion of a user's face:receiving an image from the inward-facing imaging system; analyzing theimage to identify periocular features of the user by an objectrecognizer; determining a relative position between the HMD and the headof the user based at least partly on the periocular features;determining a degree of fit by comparing the relative position with athreshold condition; and causing the HMD to perform at least one of thefollowing: adjusting a display of the HMD to compensate for the relativeposition, or providing an indication to the user associated with thedegree of fit.

In a 105th aspect, the method of aspect 104, wherein determining therelative position comprises at least one of: calculating a distance withthe HMD to eyes of the user; calculating an interpupillary distancebetween the eyes of the user; determining a relative position betweenthe HMD and the eyes of the user; determining an asymmetry of the eyesof the user; or determining a tilt of the HMD, wherein the tiltcomprises at least one of a forward tilt or a side tilt.

In a 106th aspect, the method of any one of aspects 104-105, whereinadjusting the display of the HMD to compensate for the relative positioncomprises: determining a normal resting position of the user's eye and anormal rendering position of the virtual object which corresponds to thenormal resting position of the user's eye; calculating an adjustment tothe normal rendering position of the virtual object based at leastpartly on the relative position between the HMD and the head of theuser; and determining the rendering location of the virtual object basedon the adjustment to the normal rendering position.

In a 107th aspect, the method of any one of aspects 104-106, whereindetermining the degree of fit comprises: accessing the image of theperiocular region of the user; accessing a mapping between images of theperiocular region and the degree of fit of the HMD on the user; andapplying the mapping to determine the degree of fit of the HMD.

In a 108th aspect, the method of any one of aspects 104-107, wherein theindication to the user associated with the degree of fit comprises:providing instructions to the user on improving the degree of fit;accessing an additional eye image; applying the mapping to theadditional eye image to determine an updated degree of fit; andaccessing additional eye images and determine updated degrees of fituntil an updated degree of fit passes a threshold.

In a 109th aspect, the method of any one of aspects 104-109, whereindetermining the relative position comprises: receiving a first image anda second image from the inward-facing imaging system, wherein the firstimage and the second image are acquired in sequence; analyzing the firstimage and the second image to identify periocular features specific tothe user of the periocular region in the first image and the secondimage; determining whether the periocular region is in the first imageor the second image based at least partly on the periocular features;determining the relative position based at least partly on theappearance of the periocular features in the first and the second image;and wherein the method further comprises: in response to a determinationthat the periocular region is in neither the first image nor the secondimage, providing an indication that the HMD has been removed from thehead of the user; and in response to a determination that the periocularfeatures is either the first image or the second image, indicating achange of a state for the HMD.

In a 110th aspect, an HMD comprising: an inward-facing imaging systemconfigured to image a periocular region of a user's face, wherein theinward-facing imaging system is configured to acquire at least a firstimage of the periocular region and is further configured to mask aportion of the periocular region captured in the first image; and ahardware processor programmed to: receive an image from theinward-facing imaging system; analyze the image to identify periocularfeatures of the user by an object recognizer; determine a relativeposition between the HMD and the head of the user based at least partlyon the periocular features; and determine a degree of fit by comparingthe relative position with a threshold condition.

In a 111th aspect, the HMD of aspect 110, wherein to determine thedegree of fit, the hardware processor is programmed to: access the imageof the periocular region of the user; access a mapping between images ofthe periocular region and the degree of fit of the HMD on the user,wherein the mapping was trained using a machine learning model; andapply the mapping to determine the degree of fit of the HMD.

In a 112th aspect, the HMD of any one of aspects 110-111, wherein thedegree of fit comprises at least one of: fit well, fit adequately, orfit poorly.

In a 113th aspect, the HMD of any one of aspects 110-112, wherein thehardware processor is further programmed to adjust a rendering locationof a virtual object based on the degree of fit.

In a 114th aspect, the HMD of any one of aspects 110-113, wherein thehardware processor is further programmed to provide an indication to theuser on the degree of fit at an initialization phase of the HMD.

In a 115th aspect, the HMD of aspect 114, wherein the hardware processoris further programmed to: provide instructions to the user on improvingthe degree of fit; access an additional eye image; apply the mapping tothe additional eye image to determine an updated degree of fit; andaccess additional eye images and determine updated degrees of fit untilan updated degree of fit passes a threshold.

In a 116th aspect, the HMD of any one of aspects 110-115, wherein todetermine the relative position, the hardware processor is programmedto: receive a first image and a second image from the inward-facingimaging system, wherein the first image and the second image areacquired in sequence; analyze the first image and the second image toidentify periocular features specific to the user of the periocularregion in the first image and the second image; determine whether theperiocular region is in the first image or the second image based atleast partly on the periocular features; determine the relative positionbased at least partly on the appearance of the periocular features inthe first and the second image.

Other Considerations

Each of the processes, methods, and algorithms described herein and/ordepicted in the attached figures may be embodied in, and fully orpartially automated by, code modules executed by one or more physicalcomputing systems, hardware computer processors, application-specificcircuitry, and/or electronic hardware configured to execute specific andparticular computer instructions. For example, computing systems caninclude general purpose computers (e.g., servers) programmed withspecific computer instructions or special purpose computers, specialpurpose circuitry, and so forth. A code module may be compiled andlinked into an executable program, installed in a dynamic link library,or may be written in an interpreted programming language. In someimplementations, particular operations and methods may be performed bycircuitry that is specific to a given function.

Further, certain implementations of the functionality of the presentdisclosure are sufficiently mathematically, computationally, ortechnically complex that application-specific hardware or one or morephysical computing devices (utilizing appropriate specialized executableinstructions) may be necessary to perform the functionality, forexample, due to the volume or complexity of the calculations involved orto provide results substantially in real-time. For example, animationsor video may include many frames, with each frame having millions ofpixels, and specifically programmed computer hardware is necessary toprocess the video data to provide a desired image processing task orapplication in a commercially reasonable amount of time.

Code modules or any type of data may be stored on any type ofnon-transitory computer-readable medium, such as physical computerstorage including hard drives, solid state memory, random access memory(RAM), read only memory (ROM), optical disc, volatile or non-volatilestorage, combinations of the same and/or the like. The methods andmodules (or data) may also be transmitted as generated data signals(e.g., as part of a carrier wave or other analog or digital propagatedsignal) on a variety of computer-readable transmission mediums,including wireless-based and wired/cable-based mediums, and may take avariety of forms (e.g., as part of a single or multiplexed analogsignal, or as multiple discrete digital packets or frames). The resultsof the disclosed processes or process steps may be stored, persistentlyor otherwise, in any type of non-transitory, tangible computer storageor may be communicated via a computer-readable transmission medium.

Any processes, blocks, states, steps, or functionalities in flowdiagrams described herein and/or depicted in the attached figures shouldbe understood as potentially representing code modules, segments, orportions of code which include one or more executable instructions forimplementing specific functions (e.g., logical or arithmetical) or stepsin the process. The various processes, blocks, states, steps, orfunctionalities can be combined, rearranged, added to, deleted from,modified, or otherwise changed from the illustrative examples providedherein. In some embodiments, additional or different computing systemsor code modules may perform some or all of the functionalities describedherein. The methods and processes described herein are also not limitedto any particular sequence, and the blocks, steps, or states relatingthereto can be performed in other sequences that are appropriate, forexample, in serial, in parallel, or in some other manner. Tasks orevents may be added to or removed from the disclosed exampleembodiments. Moreover, the separation of various system components inthe implementations described herein is for illustrative purposes andshould not be understood as requiring such separation in allimplementations. It should be understood that the described programcomponents, methods, and systems can generally be integrated together ina single computer product or packaged into multiple computer products.Many implementation variations are possible.

The processes, methods, and systems may be implemented in a network (ordistributed) computing environment. Network environments includeenterprise-wide computer networks, intranets, local area networks (LAN),wide area networks (WAN), personal area networks (PAN), cloud computingnetworks, crowd-sourced computing networks, the Internet, and the WorldWide Web. The network may be a wired or a wireless network or any othertype of communication network.

The systems and methods of the disclosure each have several innovativeaspects, no single one of which is solely responsible or required forthe desirable attributes disclosed herein. The various features andprocesses described above may be used independently of one another, ormay be combined in various ways. All possible combinations andsubcombinations are intended to fall within the scope of thisdisclosure. Various modifications to the implementations described inthis disclosure may be readily apparent to those skilled in the art, andthe generic principles defined herein may be applied to otherimplementations without departing from the spirit or scope of thisdisclosure. Thus, the claims are not intended to be limited to theimplementations shown herein, but are to be accorded the widest scopeconsistent with this disclosure, the principles and the novel featuresdisclosed herein.

Certain features that are described in this specification in the contextof separate implementations also can be implemented in combination in asingle implementation. Conversely, various features that are describedin the context of a single implementation also can be implemented inmultiple implementations separately or in any suitable subcombination.Moreover, although features may be described above as acting in certaincombinations and even initially claimed as such, one or more featuresfrom a claimed combination can in some cases be excised from thecombination, and the claimed combination may be directed to asubcombination or variation of a subcombination. No single feature orgroup of features is necessary or indispensable to each and everyembodiment.

Conditional language used herein, such as, among others, “can,” “could,”“might,” “may,” “e.g.,” and the like, unless specifically statedotherwise, or otherwise understood within the context as used, isgenerally intended to convey that certain embodiments include, whileother embodiments do not include, certain features, elements and/orsteps. Thus, such conditional language is not generally intended toimply that features, elements and/or steps are in any way required forone or more embodiments or that one or more embodiments necessarilyinclude logic for deciding, with or without author input or prompting,whether these features, elements and/or steps are included or are to beperformed in any particular embodiment. The terms “comprising,”“including,” “having,” and the like are synonymous and are usedinclusively, in an open-ended fashion, and do not exclude additionalelements, features, acts, operations, and so forth. Also, the term “or”is used in its inclusive sense (and not in its exclusive sense) so thatwhen used, for example, to connect a list of elements, the term “or”means one, some, or all of the elements in the list. In addition, thearticles “a,” “an,” and “the” as used in this application and theappended claims are to be construed to mean “one or more” or “at leastone” unless specified otherwise.

As used herein, a phrase referring to “at least one of” a list of itemsrefers to any combination of those items, including single members. Asan example, “at least one of: A, B, or C” is intended to cover: A, B, C,A and B, A and C, B and C, and A, B, and C. Conjunctive language such asthe phrase “at least one of X, Y and Z,” unless specifically statedotherwise, is otherwise understood with the context as used in generalto convey that an item, term, etc. may be at least one of X, Y or Z.Thus, such conjunctive language is not generally intended to imply thatcertain embodiments require at least one of X, at least one of Y and atleast one of Z to each be present.

Similarly, while operations may be depicted in the drawings in aparticular order, it is to be recognized that such operations need notbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. Further, the drawings may schematically depict one more exampleprocesses in the form of a flowchart. However, other operations that arenot depicted can be incorporated in the example methods and processesthat are schematically illustrated. For example, one or more additionaloperations can be performed before, after, simultaneously, or betweenany of the illustrated operations. Additionally, the operations may berearranged or reordered in other implementations. In certaincircumstances, multitasking and parallel processing may be advantageous.Moreover, the separation of various system components in theimplementations described above should not be understood as requiringsuch separation in all implementations, and it should be understood thatthe described program components and systems can generally be integratedtogether in a single software product or packaged into multiple softwareproducts. Additionally, other implementations are within the scope ofthe following claims. In some cases, the actions recited in the claimscan be performed in a different order and still achieve desirableresults.

What is claimed is:
 1. A head-mounted device (HMD) comprising: a spatial augmented reality (AR) display system configured to render a virtual object from a perspective of the user's eye; non-transitory memory configured to store a measurement of a relative position between the HMD and the head of the user; and a hardware processor in communication with the non-transitory memory and the spatial AR display system, the hardware processor programmed to: access the measurement of the relative position between the HMD and the head of the user; calculate an adjustment to a normal rendering position of the virtual object based at least partly on the relative position between the HMD and the head of the user; determine the rendering location of the virtual object based on the adjustment to the normal rendering position; and instruct the spatial AR display system to render the virtual object at the rendering location.
 2. The HMD of claim 1, wherein the measurement of the relative position between the HMD and the head of the user is calculated by performing at least one of: tracking the periocular features using visual keypoints, or matching a region of the face with a dense map encoding at least a portion of the head of the user.
 3. The HMD of claim 2, wherein the visual keypoints are computed using at least one of: scale-invariant feature transform, speeded up robust features, oriented FAST and rotated BRIEF, binary robust invariant scalable keypoints, or fast retina keypoint; or wherein the dense map is calculated using iterative closest point algorithm.
 4. The HMD of claim 1, wherein the hardware processor is further programmed to determine a normal resting position of the user's eye associated with a rendering viewpoint of the HMD, wherein the normal rendering position of the virtual object which corresponds to the normal resting position of the user's eye.
 5. The HMD of claim 4, wherein to calculate the adjustment to the normal rendering position of the virtual object, the hardware processor is programmed to: determine a shift with respect to the normal resting position based at least partly on the relative position between the HMD and the head of the user; and shift a coordinate associated with the rendering viewpoint of the HMD based at least partly on the shift with respect to the normal resting position.
 6. The HMD of claim 5, the relative position between the HMD and the head of the user comprises one or more of: a horizontal shift, a vertical shift, a depth shift, a tilt to a side, or a forward tilt with respect to the normal resting position.
 7. The HMD of claim 1, wherein the relative position between the HMD and the head of the user comprises a first relative position between the HMD for a first eye of the user and a second relative position for a second eye of the user.
 8. A method comprising: under control of a head-mounted device (HMD) comprising a hardware processor, a computer processor, a display system configured to render a virtual object, and an inward-facing imaging system configured to image a periocular portion of a user's face: receiving an image from the inward-facing imaging system; analyzing the image to identify periocular features of the user by an object recognizer; determining a relative position between the HMD and the head of the user based at least partly on the periocular features; determining a degree of fit by comparing the relative position with a threshold condition; and causing the HMD to perform at least one of the following: adjusting a display of the HMD to compensate for the relative position, or providing an indication to the user associated with the degree of fit.
 9. The method of claim 8, wherein determining the relative position comprises at least one of: calculating a distance with the HMD to eyes of the user; calculating an interpupillary distance between the eyes of the user; determining a relative position between the HMD and the eyes of the user; determining an asymmetry of the eyes of the user; or determining a tilt of the HMD, wherein the tilt comprises at least one of a forward tilt or a side tilt.
 10. The method of claim 8, wherein adjusting the display of the HMD to compensate for the relative position comprises: determining a normal resting position of the user's eye and a normal rendering position of the virtual object which corresponds to the normal resting position of the user's eye; calculating an adjustment to the normal rendering position of the virtual object based at least partly on the relative position between the HMD and the head of the user; and determining the rendering location of the virtual object based on the adjustment to the normal rendering position.
 11. The method of claim 8, wherein determining the degree of fit comprises: accessing the image of the periocular region of the user; accessing a mapping between images of the periocular region and the degree of fit of the HMD on the user; and applying the mapping to determine the degree of fit of the HMD.
 12. The method of claim 8, wherein the indication to the user associated with the degree of fit comprises: providing instructions to the user on improving the degree of fit; accessing an additional eye image; applying the mapping to the additional eye image to determine an updated degree of fit; and accessing additional eye images and determine updated degrees of fit until an updated degree of fit passes a threshold.
 13. The method of claim 8, wherein determining the relative position comprises: receiving a first image and a second image from the inward-facing imaging system, wherein the first image and the second image are acquired in sequence; analyzing the first image and the second image to identify periocular features specific to the user of the periocular region in the first image and the second image; determining whether the periocular region is in the first image or the second image based at least partly on the periocular features; determining the relative position based at least partly on the appearance of the periocular features in the first and the second image; and wherein the method further comprises: in response to a determination that the periocular region is in neither the first image nor the second image, providing an indication that the HMD has been removed from the head of the user; and in response to a determination that the periocular features is either the first image or the second image, indicating a change of a state for the HMD.
 14. A head-mounted device (HMD) comprising: an inward-facing imaging system configured to image a periocular region of a user's face, wherein the inward-facing imaging system is configured to acquire at least a first image of the periocular region and is further configured to mask a portion of the periocular region captured in the first image; and a hardware processor programmed to: receive an image from the inward-facing imaging system; analyze the image to identify periocular features of the user by an object recognizer; determine a relative position between the HMD and the head of the user based at least partly on the periocular features; and determine a degree of fit by comparing the relative position with a threshold condition.
 15. The HMD of claim 14, wherein to determine the degree of fit, the hardware processor is programmed to: access the image of the periocular region of the user; access a mapping between images of the periocular region and the degree of fit of the HMD on the user, wherein the mapping was trained using a machine learning model; and apply the mapping to determine the degree of fit of the HMD.
 16. The HMD of claim 14, wherein the degree of fit comprises at least one of: fit well, fit adequately, or fit poorly.
 17. The HMD of claim 14, wherein the hardware processor is further programmed to adjust a rendering location of a virtual object based on the degree of fit.
 18. The HMD of claim 14, wherein the hardware processor is further programmed to provide an indication to the user on the degree of fit at an initialization phase of the HMD.
 19. The HMD of claim 18, wherein the hardware processor is further programmed to: provide instructions to the user on improving the degree of fit; access an additional eye image; apply the mapping to the additional eye image to determine an updated degree of fit; and access additional eye images and determine updated degrees of fit until an updated degree of fit passes a threshold.
 20. The HMD of claim 14, wherein to determine the relative position, the hardware processor is programmed to: receive a first image and a second image from the inward-facing imaging system, wherein the first image and the second image are acquired in sequence; analyze the first image and the second image to identify periocular features specific to the user of the periocular region in the first image and the second image; determine whether the periocular region is in the first image or the second image based at least partly on the periocular features; and determine the relative position based at least partly on the appearance of the periocular features in the first and the second image. 